Questions and answers for HIG-18-032
General
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For the answers, the following color code is used:* green - comment answered
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- Questions and answers for HIG-18-032
- General
- Color code
- Comments from Cecile, on FF AN v3:
- Comments from Cecile, on Main AN v3:
- Comments from Martin, on Main AN v4:
- Comments from Cecile, on FF AN v4:
- Comments from Tyler, on Main AN v7:
- Comments from Cecile, on FF AN v4/v5:
- Comments from Cecile, on NN AN v4:
- Comments from Martin, on NN AN v5:
- Comments from Isobel, on Main AN v8:
- Comments from Cecile, on Main AN v8:
- Comments from Cecile, on PAS v0:
- Comments from HIG-18-032 hypernews link :
- Comments from Andrei, on AN v10 and PAS v2:
- Comments from the ARC v1, Part I: Physics
- Comments from the ARC v1, Part II: Text
- Comments from the ARC v2
- Comments from the Anne-Catherine on PAS v5
- Comments from the ARC on PAS v6
- Comments from the LE on PAS v7
Comments from Cecile, on FF AN v3:
http://cms.cern.ch/iCMS/jsp/openfile.jsp?tp=draft&files=AN2018_257_v3.pdf
- Figures with stacked histograms: please increase the size of the legend (impossible to read even in A4 format), and possibly increase the size of the figures
- The documentation does not align with the twiki and sometimes contradicts itself. In particular, is the prompt contribution subtracted (eq 1, l173/174)? are the FF binned in decay mode (l146, l283-286, l295-296)? are the fractions renormalised to the sum of fake fractions (l171)? Please correct and double check everything!
- Related to what I understand from the twiki but is not transparent in this note. You recommend to compute the fractions in bins of mvis and njets: the fractions for tt, qcd, and w in generally do not sum to 1.0 because there is also a fraction or real taus. Because the fractions do not sum to 1.0 you do not need to subtract the prompt contribution (what you are doing in practice is applying a fake factor of 0 to events with real taus). But the fractions can depend on other variables that you use in the analysis. Let’s take nbtag as an example. When you derive the fractions in the inclusive region, you can have something like f_qcd=0.2, f_w=0.5, f_tt=0.1, f_real=0.2 for mvis under the ZTT peak. But for events where there is nbtag>0 (selected preferentially by the NN to populate the ttbar CR) the fractions you should have used could have been instead f_qcd=0.05, f_w=0.3, f_tt=0.6, f_real=0.05. Assuming FFqcd=FFw=FFtt you end up with a 15% difference because you considered there was 20% real taus instead of 5%. If FFqcd!=FFw!=FFtt the difference is even bigger. In my opinion a much better way to proceed is to renormalize f_qcd, f_w and f_tt so they always sum up to 1. Then you need to apply the sum of fake factors also to real events in the AR and subtract them from the reweighed data. In that case f_real is always correct, for any choice of variable. Please comment.
- The subtraction of real taus/leptons on the fly would also allow us to treat correctly all systematics like the tau energy scale tau ID efficiency, which are otherwise neglected to determine the fractions even if not negligible. Please comment.
- l26: I think the integrated luminosity corresponding to this json is 41.4 fbinv
- l45/46: Why do you use these two single tau triggers? They are not part of the baseline selection for the analysis and should not bring you much acceptance
- l64: in 2017 there is no non-triggering MVA ID anymore
- Table 3: the cross sections should differ from 2016 because of different tune
- l70: why don’t you also derive fake factors for 20 < pt < 30 GeV? That would allow more flexibility in the analysis, and application to more different analyses
- l95: this is not really true; in the Z mass peak region you can have 20% real taus in the AR
- l111: Have you checked if the fake factors would be different for njets=1 and njets>1?
- eq 1: according to the twiki and to part of the discussion later in the AN you do NOT subtract n_true
- l133: how do you choose the thresholds for the isolation?
- l135: the mT cut is the same in the DR and SR, no need to write it
- l145: “is negligible” -> do you mean you neglect it? From the plots it is larger than 10%, which is not what I call negligible
- l146: I understand there is no decay mode dependence anymore
- Figure 2: for the tautau plot, do you require exactly one anti-isolated tau, whichever it is?
- l153: I thought the SR cut was mt < 50 GeV
- l153: why don’t you go higher in mt in the DR to have a better purity?
- figure 4 and others: is it the tau pt or the jet pt? I believe it is tau pt, please correct everywhere
- figure 4: how can you explain the pt dependence in the tautau case (even if it is a different WP)?
- figure 4 and others: how do you choose the binning? If the bins have different widths you need to indicate the horizontal error bars. Why do you go higher in pt in the case of etau >=1 jet than mutau >=1 jet?
- figure 4 and others: how did you choose the fit function (Landau + polynomial)? Is the polynomial a normal one (=line if order one) or a special one (Bernstein, Chebychev, …)? Do you extrapolate with a constant at high tau pT?
- figs 5 and 6: here you go as low as tau pT = 20 GeV
- l159: why don’t you bin the TT fake fractions in bins of njets? You should have enough statistics for njets=0 too.
- l171: this is in complete contradiction with the instructions in the twiki!! According to the twiki, you don’t renormalize to the sum of fractions for fake backgrounds (no denominator), which is the only reason why you are allowed not to subtract the prompt contribution from the AR.
- l173/174: I thought there was no subtraction anymore?!
- l185-189: again this applies only if you make a subtraction, which is not the case according to the twiki
- l201: why don’t you bin the corrections in njets too? Can you demonstrate that all the corrections described in this chapter would be the same for njets=0 and njets>0?
- l208: the differences from the finite binning are supposed to be covered already by the total uncertainty (the one described in l293)
- 212: why do you expect the fake factors to depend on the light lepton isolation?
- l215: observed -> calculated
- l218: why do you expect a non closure with mvis?
- figure 9: indeed all these corrections look very compatible with a flat line centered at 1. Are you sure you are not overfitting? Please indicate the chi2/ndof for your correction and for a horizontal line to judge if you are overfitting. This applies to all the corrections throughout the AN.
- l238-242: please show the data and predicted MC in all these regions (OS anti-isolated, …) and the raw FF obtained here
- l248-253: given your explanation of the mT dependence, why is it not covered by the binning in njets (low mT means more jets)?
- figure 12 left: the average correction seems well below 1. How is that possible if you apply the fake factors derived using the same events and the fit functions match the data points very well in Fig 7? I could expect a shape dependence with mvis but not a correction of the normalisation.
- l265: Z->mumu + jets
- l260-266: please add plots and numbers related to this check
- fig13: How can you relate these correction shapes to your explanation in l248-253?
- fig 13: don’t you think a flat line would make as good a job with less overfitting? please check as mentioned already for fig 9. This wavy correction needs to have a physical explanation.
- l271: is it a problem if it overlaps with the emu signal region, even if we don’t use FF in this final state?
- l283-286: so you split in DM??
- l295-296: are you sure it is 12 and 4 if there is no DM dependence anymore? If there is no DM dependence, what are these DM-dependent uncertainties still prescribed in the twiki??
- l293-296: How can you end up with only one uncertainty per final state per jet multiplicity if the fit function has 4(?) degrees of freedom? In principle you should have as many uncertainties per function as the number of degrees of freedom. Otherwise the low pT region for example can constrain the high pT region without any good physical reason. This question applies also to the uncertainties in the corrections; please answer this question for all uncertainties
- fig 16: don’t you think the uncertainty band at low pt is too small compared to the size of the error bars on the points?
- l316-318: What is that? where is it explains? How do you determine it? (uncertainty on fractions)
- l319-320: again is there or not a subtraction???
- l325-327: please give the details on how you arrive to these numbers
- fig 17 and other control plots: please reduce the range of the y axis in the ratio plots
Comments from Cecile, on Main AN v3:
http://cms.cern.ch/iCMS/jsp/openfile.jsp?tp=draft&files=AN2018_255_v3.pdf
, Appendix D. An analysis of Taylor coefficients of the neural net output as function of inputs was performed.
The variables bpt_1 and bpt_2 themselves show some sensitivity for process separation (values mostly around 0.05, roughly between 0.02 to 0.11), see Figure 23-28.
More important are the correlations of bpt_1,2 with m_sv and/or m_vis (see Tables 5-10) with higher values around 0.15.
Based on this we've decided to keep bpt_1 and bpt_2 as variables supporting nbtag variable, as long as they pass the GOF test.
An extensive study of b-jet pt's on the full analysis from retraining of the neural net up to constraints on r wasn't done, because the study of Taylor coefficients presented in AN-2018/256 is considered as sufficient.
How can I translate Taylor coefficients to an impact on the sensitivity? How can you explain that there is a correlation between bpt_2 and m_vis, and then that it matters?
Details on the taylor coefficients study are described here: https://arxiv.org/abs/1803.08782 (1) and were also presented in the HTT meeting: https://indico.cern.ch/event/718595/contributions/3000074/ (2).
To summarize this briefly: The output of the neural net, so the various scores for classification, is considered as a vector function of the vector of inputs:
![$\vec{f}(\vec{x}) = [ggH(\vec{x}), qqH(\vec{x}), ..., misc(\vec{x})]^T$](/twiki/pub/Main/SMHTautauML2017Review/latex63fbec3dd551a4debd2c08d399b069e9.png)
with ![$\vec{x} = [pt_1, pt_2, ..., m_{vis}, m_{sv}, ...]^T$](/twiki/pub/Main/SMHTautauML2017Review/latex57ad4091aea8ba6c34701a6b64a2a04b.png)
(corresponding to selected variables)
A Taylor coefficient of this function's Taylor expansion would give information about how strongly would the classification score change, if we would vary the inputs. This allows to quantify, how important a variable (coefficients of 1st derivatives) or the correlation of two variables (coefficients of 2nd derivatives) is to classify an event to a certain class.
Several examples are shown on slides 4,5 of (2) to demonstrate possible use-cases. They show, that the measures introduced (means across the used dataset for absolute values of the 1st & 2nd taylor coefficients) can be used to quantify, how well a 1D or 2D variable could be used to assign an event to certain class.
So in other words, these coefficients can quantify the separation power of variables, which is learned by the network. In that sense, they can quantify (indirectly) the impact on the analysis sensitivity.
We did studies with leaving important variables (like m_vis) out of the training and saw decrease in the expected constraint on r. Since such a study is a major effort (1-2 days), this cannot be done on a short time-scale for several combinations of variables. If required, we would propose to do such studies after the pre-approval talk for a requested set of variables (both 1D and 2D).
Concerning the example of bpt vs. m_vis:
Please have a look at the inclusive 2D plots in et channel for some processes:
.
I have troubles understanding what the plots exactly show. I think the title gives the category, but where can I see which process it is?
The plots always show total background. I think it would be a mess of plots to show it for every background separately but please feel free to make suggestions. Maybe to take just the backgrounds affected by this uncertainty?
.
. However, this needs technical adaptions to handle unrolled distributions properly to match our needs.
We will work on the implementation in the next days.
Closed here as it is discussed below as v8 comment
ggH signal is peaking between 0.4 and 0.5 with large tales to 1, whereas the backgrounds are peaking between 0.3 and 0.4 with less prominant tails to 1. Hardest background to distinguish from ggH in this ggH inclusive category (no stage 1 splitting): genuine taus from embedding (EMB). However, a shape difference between ggH and EMB is already visible by eye.
The prefit distribution on the left is tranformed by the fully combined bg-only fit (all channels, 2016+2017) into the postfit distribution on the right. Most prominent change is visible in the FF background, so the FF related systematics for this year, channel and category (both normalization & shape-changing) are pulled. This is compatible with other background categories as can be seen in the prefit and postfit distributions (the same fully combined bg-only fit): http://ekpwww.etp.kit.edu/~wunsch/2018-12-12/blinded_backgrounds/index.php
As a next step a fit was performed for the signal POI r on asimov with signal for fixed values of ttbar xsec uncertainty nuisance: tjXsec=-0.99, tjXsec=0.01 (ca. best fit), tjXsec=1.01. Please note, this nuisance is already normalized to prefit sigma, i.e. the prefit sigma equals to 1. Results for the subset of nuisances shown above, ordered by correlation to r, can be seen here:
http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_12/tjXsec-0.99_diff_nuisances_selected.html
http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_12/tjXsec0.01_diff_nuisances_selected.html
http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_12/tjXsec1.01_diff_nuisances_selected.html
In general it is difficult to quantify and deduce the interplay between all nuisances playing a role. However, what can be observed is for instance the fact, that with higher values of the ttbar nuisance parameter, the luminosity uncertainty nuisance as well as some other lnN uncertainties nuisances are pulled down. The signal is affected by the lumi nuisance directly. In turn, there is more "space" for r to go up, after lumi had a downward pull. Again: this is only an example how it might happen, that the ttbar xsec has a strong impact on the signal strength going into the same direction.
Switching to lnN uncertainties however does not seem to be an option as there are rather clear shape effects in some cases e.g. in the et ZLL category.
You need to find a way to smoothen the shapes to remove the artificial constraints.
Closed here as it is discussed as comment on v8 below
- General: What is the status of the data card level synchronization?
- General: Can you consider to simply veto b jets to remove the overlap with ttH? This is not urgent but in view of the combination
- General: You should add a section to explain why you use each of the training variables (= why it is supposed to help to separate the processes).
- General: Some variables, like the b jet pT, do not seem useful to me to separate the processes. Can you quantify how much each variable helps (sort of ranking)? Have you tried to remove the variables that look useless and compare the expected results?
, Appendix D. An analysis of Taylor coefficients of the neural net output as function of inputs was performed.
The variables bpt_1 and bpt_2 themselves show some sensitivity for process separation (values mostly around 0.05, roughly between 0.02 to 0.11), see Figure 23-28.
More important are the correlations of bpt_1,2 with m_sv and/or m_vis (see Tables 5-10) with higher values around 0.15.
Based on this we've decided to keep bpt_1 and bpt_2 as variables supporting nbtag variable, as long as they pass the GOF test.
An extensive study of b-jet pt's on the full analysis from retraining of the neural net up to constraints on r wasn't done, because the study of Taylor coefficients presented in AN-2018/256 is considered as sufficient.
How can I translate Taylor coefficients to an impact on the sensitivity? How can you explain that there is a correlation between bpt_2 and m_vis, and then that it matters?
Details on the taylor coefficients study are described here: https://arxiv.org/abs/1803.08782 (1) and were also presented in the HTT meeting: https://indico.cern.ch/event/718595/contributions/3000074/ (2).
To summarize this briefly: The output of the neural net, so the various scores for classification, is considered as a vector function of the vector of inputs:
with (corresponding to selected variables)
A Taylor coefficient of this function's Taylor expansion would give information about how strongly would the classification score change, if we would vary the inputs. This allows to quantify, how important a variable (coefficients of 1st derivatives) or the correlation of two variables (coefficients of 2nd derivatives) is to classify an event to a certain class.
Several examples are shown on slides 4,5 of (2) to demonstrate possible use-cases. They show, that the measures introduced (means across the used dataset for absolute values of the 1st & 2nd taylor coefficients) can be used to quantify, how well a 1D or 2D variable could be used to assign an event to certain class.
So in other words, these coefficients can quantify the separation power of variables, which is learned by the network. In that sense, they can quantify (indirectly) the impact on the analysis sensitivity.
We did studies with leaving important variables (like m_vis) out of the training and saw decrease in the expected constraint on r. Since such a study is a major effort (1-2 days), this cannot be done on a short time-scale for several combinations of variables. If required, we would propose to do such studies after the pre-approval talk for a requested set of variables (both 1D and 2D).
Concerning the example of bpt vs. m_vis:
Please have a look at the inclusive 2D plots in et channel for some processes:
- http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_11/bpt_1_vs_m_vis/index.html (default at bpt_1 = -10 included)
- http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_11/bpt_1_above_0_vs_m_vis/index.html (default at bpt_1 = -10 excluded)
- Table 2: The EWK samples are missing
- Table 3: Shouldn’t the ZLL cross section be different between 2016 and 2017 because of different tunes?
- Table 3: Please add the Wgamma sample names
- Table 3: What does * * * mean (not in the caption)?
- l98: can you clarify why this ele ID choice makes it “simpler”?
- l118: Why is the electron isolation different between 2016 and 2017? Why not align?
- l187: There is also a very very loose WP in 2017
- l191: ML->MVA to remove the confusion with the ML used to extract the signal
- l242: You should move to the latest JEC (v32)
- l261: I have noticed in the sync ntuples that KIT does not cut the b jet pseudo rapidity at 2.4. Can you confirm everything is done correctly in the analysis?
- l263: Danny reports a bad data/MC agreement using deepCSV in 2017. Have you checked?
- Table 7: I thought you also use the single electron 35 trigger in 2017
- l353: Have you tried to reoptimize the Dzeta cut? Is it still the best cut?
- l370: isn’t it tight instead of medium?
- l370: do you really apply the tight tau ID before choosing the best pair? How do you do to select the anti-isolated events needed for the FF method?
-
- In 2016, we use the default 2015 trainings; in 2017, the recomputed on top of MiniAOD 2017v2 trainings. Since there are several developments ongoing for the Tau ID (new DM vs. old DM, several trainings, new methods like DeepPFTau etc., one ID for all years proposal, ...), an extensive Tau ID optimization would make sense in the scope of Run II legacy results. For this analysis, we decided to stick to the chosen ID's above ( best choice for the sample sets used).
- 2016 working-point range: VLoose to VTight; 2017 working-point range VVLoose to VVTight. To be aligned with the NanoAOD production of 2017 samples (search for nTau), we choose for 2017 data/MC VVLoose working point during pair selection.
. -
- Signal region (both 2016 & 2017): Tight WP of the corresponding byIso Tau ID
- Anti-Isolated region for FF (both 2016 & 2017): VLoose WP of the corresponding byIso Tau ID
- l384: Do I understand correctly that you require ptH>50 in 2017 and ptH>50 && pt_1>50 in 2016? Please reformulate so it is crystal clear. Why do you apply this ptH cut? I think it was not applied in 2016. How was it optimised? And if I understood the cuts correctly, why don’t you ask pt_1>50 in 2017 too?
- l391: This is not completely correct as you separate the events so there is no overlap for the triggers. There should be an upper pT cut for the events associated to the cross trigger
- l422: same as above
- l495: Your T background contains any tau decay. How do you treat the lnN uncertainties in that case (e.g. only a fraction of these events should have a tau ID uncertainty, a µ->tau ID uncertainty, or an e->tau ID uncertainty)?
- l502: Does this mean that there is no imposed tau decay (e.g. only hadronic) in the embedded samples?
- l516: the numerator and denominator are not probabilities but numbers of events. Please reformulate.
- l528: I thought you don’t use tau pT < 30 GeV
- l582: please add more details (or copy paste from [20])
- l584: Is the shape taken from SS with a relaxed isolation to increase the statistics?
- Section 6.5. I need to read in more details. But have these been presented to EGamma and Muon POG? Please always plot data/MC in the ratio plot and not the opposite (e.g. fig 12)
- Table 10: You should consider rounding the uncertainty so it is the same for all decay modes
- l797: please show the equivalent for 2017
- l875: Can you show the shifted templates for all shape uncertainties?
I have troubles understanding what the plots exactly show. I think the title gives the category, but where can I see which process it is?
The plots always show total background. I think it would be a mess of plots to show it for every background separately but please feel free to make suggestions. Maybe to take just the backgrounds affected by this uncertainty?
- l875: Can you prove that your ML has a linear effect on the shape uncertainties? What I mean: does twice the variation of the 1 sigma shifted template correspond to the shifted template if you shift the input by 2 sigmas?
- l886: Have you checked that the shifted templates corresponding to your JES have smooth variations compared to your nominal template? If this is not the case you should consider smoothing them or replacing them by lnN depending on how they look like.
- l919: Have you made sure that the shifted templates still have the same normalisation as the nominal template?
- l924: You should not correlate 2016 and 2017 since the tunes are different (and I guess the corrections look very different too)
- l924: isn’t it conservative given the statistics you have to derive the corrections?
- l925: Does it really have a shape effect? Cant you replace the shapes by lnN?
- l935: Why is it fully correlated between 2016 and 2017?
- l936: How do you correlate 2016 and 2017?
- l944: Is it really 6 if there is no DM splitting anymore?
- l975: Haven’t you forgotten the electron energy scale uncertainty?
- l982: It is hard to understand what you do with the tau ID uncertainty. In 2016 there were two parts: one fully correlated between DM and one fully uncorrelated. Now the TAU POG recommends 50% correlation between 2016 and 2017. Do you split both 2016 uncertainties into 2?
- l1007: is 10% recommended by the TAU POG?
- l1005: why isn’t there a different uncertainty for the single lepton and the cross triggers? They affect the phase space differently, which could have an impact on the muon/electron pT spectrum for example.
- l1011: why 4% for the double mu trigger while all the other triggers before had only 2%?
- l1014 (and actually everywhere): please add references for all the uncertainty values
- l1020: why isn’t there at least a partial correlation?
- l1027: are you sure of this number? Please add the reference
- l1030: same as above
- l1036: any plot to support this statement?
- Table 14: You can round to have the same uncertainties in 2016 and 2017
- l1083: even after reading the whole note I do not understand your criteria for checking “by eye”
- l1084: why do you need to remove outliers?
- Fig 25 and others: Why don’t you remove the isolation from the GOF? You know it is going to fail and why, and it is therefore not used in the NN
- Fig 25 and others: Are all systematic uncertainties included when running the GOF?
- Fig 25 and others: How is the signal treated in the GOF?
- Fig 25 and others: What kind of GOF is performed?
- Fig 32, Fig 34: You have a significant number of 2D tests failing when involving the ditau mass, but you do not remove this variable. Please comment.
.
- Fig 36: Same as above. 7/16 tests have pvalue <= 0.05!
- Table 16: the ditau mass in etau looks pretty bad in Fig 72. How can you explain it passed the GOF? Is it because it is a saturated GOF and not the one that cumulates the variations over neighbour bins)?
- l1105: please define “major background”
- l1122: I understand why you do not use the FF background for the training in etau/mutau. But why not use it in tautau, where you don’t separate the fake backgrounds in classes?
- l1122: The ttbar contamination in the embedded samples should be small enough for it not to be a problem. If the major problem is too many embedded samples, you could train with a fraction of them only.
- l1127: Can you quantify “suboptimal”?
- Table 17: What enters misc in emu? In general can you please define misc in each case?
- l1129: I do not understand at all this sentence.
- l1148: not for now but for the full run-2 paper. Would you like to ask for a bigger simulation to be able to perform a training with several classes based on STXS stage 1?
- Section 11: How do you define the binning for each of the subcategories?
. However, this needs technical adaptions to handle unrolled distributions properly to match our needs.
We will work on the implementation in the next days.
Closed here as it is discussed below as v8 comment
- Section 11: Can you please add an explanation on how you choose the lower bound of the histograms? I only understood that yesterday during the working meeting.
- Section 11: Are all uncertainties taken into account in the uncertainty band (using Combine Harvester?)?
- Fig 41: Please also show the VH signal, at least in the categories targeting VH.
- Fig 42 and others: Can you show in linear scale and normalised to unity the signal and backgrounds in the ggH categories? The shapes look really the same to me now. If the shapes are so close, don’t you think you could find a better separating variable?
ggH signal is peaking between 0.4 and 0.5 with large tales to 1, whereas the backgrounds are peaking between 0.3 and 0.4 with less prominant tails to 1. Hardest background to distinguish from ggH in this ggH inclusive category (no stage 1 splitting): genuine taus from embedding (EMB). However, a shape difference between ggH and EMB is already visible by eye.
- Fig 51 bottom left: The agreement looks bad. Please comment.
The prefit distribution on the left is tranformed by the fully combined bg-only fit (all channels, 2016+2017) into the postfit distribution on the right. Most prominent change is visible in the FF background, so the FF related systematics for this year, channel and category (both normalization & shape-changing) are pulled. This is compatible with other background categories as can be seen in the prefit and postfit distributions (the same fully combined bg-only fit): http://ekpwww.etp.kit.edu/~wunsch/2018-12-12/blinded_backgrounds/index.php
- Fig 53: In this first bin I am not sure I understand what is the y axis. I would guess it is one event divided by the bin width (0.8), which should be a point above 1 instead of below 1. Can you please explain how the number of events per bin is computed?
- Section 11: You certainly know it but 2016 emu plots and 2017 all plots are missing
- Section 12: Can you please provide me with the full impact plot?
- l1173: How is VH treated? Is it part of what you call qqH in Table 18? Or is it floated like a background? It would make sense to have a VH line in this table and have another coupling strength modifier
- Fig 58: what is uncertainty 2? Is it the ttbar cross section? Why is is ranked so high?
As a next step a fit was performed for the signal POI r on asimov with signal for fixed values of ttbar xsec uncertainty nuisance: tjXsec=-0.99, tjXsec=0.01 (ca. best fit), tjXsec=1.01. Please note, this nuisance is already normalized to prefit sigma, i.e. the prefit sigma equals to 1. Results for the subset of nuisances shown above, ordered by correlation to r, can be seen here:
http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_12/tjXsec-0.99_diff_nuisances_selected.html
http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_12/tjXsec0.01_diff_nuisances_selected.html
http://ekpwww.etp.kit.edu/~akhmet/plots_archive/2018_12_12/tjXsec1.01_diff_nuisances_selected.html
In general it is difficult to quantify and deduce the interplay between all nuisances playing a role. However, what can be observed is for instance the fact, that with higher values of the ttbar nuisance parameter, the luminosity uncertainty nuisance as well as some other lnN uncertainties nuisances are pulled down. The signal is affected by the lumi nuisance directly. In turn, there is more "space" for r to go up, after lumi had a downward pull. Again: this is only an example how it might happen, that the ttbar xsec has a strong impact on the signal strength going into the same direction.
- Fig 58 lines 17 and 22: why is there qqh in the names of these FF uncertainties?
- Fig 58 line 5: the jet energy scale is still constrained. It may be due to artificial constraints due to spiky templates as I wrote before. Please check where the constraints come from. The JES are not applied to FF and embedded so there should not be a constraint.
Switching to lnN uncertainties however does not seem to be an option as there are rather clear shape effects in some cases e.g. in the et ZLL category.
You need to find a way to smoothen the shapes to remove the artificial constraints.
Closed here as it is discussed as comment on v8 below
- Fig 58: Can you please justify all the constraints in the text?
- Table 19: The 0 jets measurement is less good than that from HIG-16-043 (which can be further improved by having the tau pT as a second dimension instead of the decay mode). Can you explain why the ML performs worse? I know 0 jet is not the most important category, but you could consider to have a cut-based 0 jet category? This would demonstrate also that you control all your backgrounds well (ZTT peak, ZL peak, …).
- Fig 60: I guess the fake background is dominating at high electron pT. If so there is a 20% overestimation of the background by the FF method. This is also probably why you reject most of the pT distributions from the GOF in 2017. Can you investigate that?
- Fig 72: As said before I am surprised the ditau mass does not fail the GOF test. Can you explain why?
- Comparing Fig 68 and 80: You have twice as many events in 2016. Why?
- Comparing Fig 78 and 66: You have twice as many events in 2017. Why? Given the muon pT spectrum it cannot only be due to the trigger. Same question for etau.
- l871: What about VH and VBF?
- l451: I think the pair ordering biases the application of the FF method. For the FF to work you need to treat the anti-isolated events in exactly the same way as the isolated events. However you favour isolated events through the pair ordering, which creates a “deficit” in the anti-isolated region. I think that if the FF method is used the pairs should be ordered based on pT only (or also light lepton isolation but not tau isolation). Please comment.
Comments from Martin, on Main AN v4:
http://cms.cern.ch/iCMS/jsp/openfile.jsp?tp=draft&files=AN2018_255_v4.pdf
- Table 2: k factor for * * * is missing
- Table 3: sample name for Wgamma is missing
- l 98: makes simpler -> makes definition of isolation-based sideband regions possible, as required e.g. by the fake factor method
- Table 4: can you clarify which of the values are for the WP of which year in the AN?
- 192: hadronic tau decays is used
- 252: please point out that it is a POG recommendation to use the tight WP for 2017 (otherwise it seems arbitrary that you do not use the same as for 2016)
- 284: (emu) So one of the triggers for data is not used in simulated samples? How is that corrected for? Via trigger SFs? Please explain in the AN.
- 316: (emu) For 2016 electron electron ID, it says you use ID that includes isolation variables. Is that correct? I thought this ID exists only for 2017.
- 340: same comment as above
- 349: of the event
- 358: has to be
- 370: it says the medium WP is used for tautau, but l. 192 states that tight is used everywhere.
- 385: why the additional requirement in 2016, and not in 2017?
- 481: object*s*
- 5.4: Please give the range in which the OS/SS correction numbers are, and state how uncertainties are determined.
- 595: please give the typical efficiency loss of this jets removal (i.e. something like "about X% for VBF and below Y% for all other samples"
- 629: event*s*
- 724 (or elsewhere): give typical values (or a range) for the embedding SF for both 2016, and 2017
- 798: is the same weight applied for 2016 and 2017, or do you derive them separately?
- Fig 25: pt(e) is badly modeled, pt(tau) is fine; yet Table 15: you use pt(e), not pt(tau) ??
- S 7.1: The subsection is only about theory signal uncertainties, right? Then please rename it to "Signal theory uncertainty"
- 994: Does that mean BBB uncertainties are not used for non-MC backgrounds (embedding, FF)?
- S 7: Concerning correlations between 2016 and 2017: whereever your decision is based on a POG recommendation, please say so in the text.
- 1110: Please describe already here the strategy to deal with variables which fail some of the 2D checks
- 1157: different treatment between -> discrepancy between
- S 9: This needs a bit more detail, even if it is in a different AN - just very briefly describe the architecture (preprocessing, activation function, number of hidden layers, number of nodes in these layers, type=fully connected feed-forward NN)
- 1175: node (without s)
Comments from Cecile, on FF AN v4:
http://cms.cern.ch/iCMS/jsp/openfile.jsp?tp=draft&files=AN2018_257_v4.pdf
- General: I see some changes in the FF wrt v3 of the AN (I guess due to adding the bins with 20 < pt < 30 GeV) but the corrections have not changed at all. Why?
- General: Have the changes been propagated to the analysis (to the FF repository) yet?
- General: I understand you have only one shape uncertainty per “uncorrected FF” (see also followup on your answer). If so can you add to all plots like Figure 5 the Up and Down FF corresponding to ± 1 sigma?
- General: If the uncorrected FF are fit with a flat line at high pT (e.g. Figure 5), why does the uncertainty band have this shape in this pT range and not a linear shape too? (BTW how do you define “high pT”?)
- General: Why do you expect the FF and their corrections to differ between etau and mutau?
- l31: write explicitly that the choice of Powheg/Madgraph depends on the data period
- l69: I thought you used pT > 20 GeV everywhere now
- l75: choosing the most isolated object biases your FF; the probability to be isolated will be higher if you favour isolated taus
- l121 and 172: I do not understand how you make the prompt subtraction. Can you clarify? I guess you take anti-isolated MC events, reweigh them with the FF, then subtract the result from the reweighed data. Is that correct? The text seems to indicate that you just take MC events from the SR without reweighting.
- l189 and about the fractions in general: I think you derive the fractions in an inclusive category. But the fractions depend on the categories (e.g. much more ttbar in a given mvis bin for events that enter the ttbar CR). How do you take that into account? Do you have an uncertainty on the fractions? Or could you compute the fractions per mvis bin of the CR/SR defined by the NN?
- Figure 5: The mt fits look bad.
- Figure 10 and others: please add the horizontal error bars for the corrections too
- Figure 12: How do you explain the difference between etau and mutau?
- Figure 19 top right: The agreement is bad in the tail
- Figure 19 center right: The agreement is bad too. Can the uncertainties really cover it (I mean pulling only one uncertainty instead of pulling each bin by 1 sigma)?
- Figure 23: The FF are about 50% higher than in 2017. This is more an analysis question, but was it considered to use an isolation WP that would have the same FF for both 2016 and 2017? Since the 2016 WP was optimised, the 2017 WP might be suboptimal.
- Figure 31: How can the correction be much below 1 for all mvis values if you apply the FF measured in exactly the same region? If this is really the same region there is obviously a problem.
- The documentation does not align with the twiki and sometimes contradicts itself. In particular, is the prompt contribution subtracted (eq 1, l173/174)? are the FF binned in decay mode (l146, l283-286, l295-296)? are the fractions renormalised to the sum of fake fractions (l171)? Please correct and double check everything!
- Indeed due to some changes we implemented following your comments in the HTT meeting there were quite a few inconsistencies. They should all be resolved now in the AN.
- Can you clarify in the AN exactly how the subtraction is done?
- Indeed due to some changes we implemented following your comments in the HTT meeting there were quite a few inconsistencies. They should all be resolved now in the AN.
- Related to what I understand from the twiki but is not transparent in this note. You recommend to compute the fractions in bins of mvis and njets: the fractions for tt, qcd, and w in generally do not sum to 1.0 because there is also a fraction or real taus. Because the fractions do not sum to 1.0 you do not need to subtract the prompt contribution (what you are doing in practice is applying a fake factor of 0 to events with real taus). But the fractions can depend on other variables that you use in the analysis. Let’s take nbtag as an example. When you derive the fractions in the inclusive region, you can have something like f_qcd=0.2, f_w=0.5, f_tt=0.1, f_real=0.2 for mvis under the ZTT peak. But for events where there is nbtag>0 (selected preferentially by the NN to populate the ttbar CR) the fractions you should have used could have been instead f_qcd=0.05, f_w=0.3, f_tt=0.6, f_real=0.05. Assuming FFqcd=FFw=FFtt you end up with a 15% difference because you considered there was 20% real taus instead of 5%. If FFqcd!=FFw!=FFtt the difference is even bigger. In my opinion a much better way to proceed is to renormalize f_qcd, f_w and f_tt so they always sum up to 1. Then you need to apply the sum of fake factors also to real events in the AR and subtract them from the reweighed data. In that case f_real is always correct, for any choice of variable. Please comment.
- We agree. We have switched to “renormalisation and subtraction”, as suggested.
- This does not solve the problem if FFqcd!=FFw!=FFtt, see my general comment above
- We agree. We have switched to “renormalisation and subtraction”, as suggested.
- l133: how do you choose the thresholds for the isolation?
- We look at the distributions such that we exclude as much as possible of the true-tau contribution while still keeping a minimum level of events in the CR needed to do a proper measurement.
- Do you have a more quantitative answer?
- We look at the distributions such that we exclude as much as possible of the true-tau contribution while still keeping a minimum level of events in the CR needed to do a proper measurement.
- figure 4: how can you explain the pt dependence in the tautau case (even if it is a different WP)?
- I assume you mean compared to etau/mutau? This is an effect of the triggers used (mostly single-lepton with a bit of lep+tau vs di-tau).
- Can you please explain how the triggers influence the pT dependence? The isolation at trigger level should be looser than the VL WP of the tau iso.
- I assume you mean compared to etau/mutau? This is an effect of the triggers used (mostly single-lepton with a bit of lep+tau vs di-tau).
- figure 4 and others: how do you choose the binning? If the bins have different widths you need to indicate the horizontal error bars. Why do you go higher in pt in the case of etau >=1 jet than mutau >=1 jet?
- Horizontal bars have been added. The binning is chosen small enough at low pt to describe the dependency in this region well, and at high pt entirely dictated by statistics, i.e. we merge bins until we get data points with uncertainties which allow for a reasonable fit. We do not have an upper cut on pt, so the range we cover only depends on events available in the DR. The data points are positioned at the gravitational centers of the bin.
- Can it be done in a more quantitative way instead of by eye? What do you mean by “reasonable fit”?
- Horizontal bars have been added. The binning is chosen small enough at low pt to describe the dependency in this region well, and at high pt entirely dictated by statistics, i.e. we merge bins until we get data points with uncertainties which allow for a reasonable fit. We do not have an upper cut on pt, so the range we cover only depends on events available in the DR. The data points are positioned at the gravitational centers of the bin.
- 212: why do you expect the fake factors to depend on the light lepton isolation?
- In particular for QCD, the two objects which are reco’ed as tau and electron are correlated (e.g. through their flavor, g/u/d/c/s/b; or through their pt due to momentum balance, which also enters the relative isolation) and this can be parametrized e.g. via the light lepton isolation. It took us a while to find out that this is needed to achieve any reasonable modeling.
- Can you add the explanation to the AN?
- In particular for QCD, the two objects which are reco’ed as tau and electron are correlated (e.g. through their flavor, g/u/d/c/s/b; or through their pt due to momentum balance, which also enters the relative isolation) and this can be parametrized e.g. via the light lepton isolation. It took us a while to find out that this is needed to achieve any reasonable modeling.
- figure 9: indeed all these corrections look very compatible with a flat line centered at 1. Are you sure you are not overfitting? Please indicate the chi2/ndof for your correction and for a horizontal line to judge if you are overfitting. This applies to all the corrections throughout the AN.
- We are not sure if a chi2/ndof is a meaningful test for a smoothing algorithm (as opposed to a fit). We agree that several of the corrections are compatible with 1. Now we still need to assign an uncertainty, and we are not sure if e.g. we get 1.05 and an uncertainty of 0.2 it is better to have the correction as 1.0+0.25/-0.15, or as 1.05+/-0.2 (as we have now). In any case, the difference in practice is very small: In the region where most of our data is, the uncertainty is small by definition and the smoothing will be close to this value.
- I agree that chi2 does not work for the smoothing. But you should first check if a constant or linear function makes a good job (here you use a chi-square test) before overfitting your corrections with unphysical shapes. If the chi-square of the constant or the linear function is not good enough, then only you use the smoothing. I agree from your example that 1.0+0.25/-0.15, or as 1.05+/-0.2 look as good as each other, but the problem is that you introduce shape effects that are not covered by systematic uncertainties if you correct by wavy unphysical corrections (e.g. the correction in one mT region is artificially high because of a statistical fluctuation, but the uncertainty on the correction can only shift the whole correction and not that particular mT bin).
- We are not sure if a chi2/ndof is a meaningful test for a smoothing algorithm (as opposed to a fit). We agree that several of the corrections are compatible with 1. Now we still need to assign an uncertainty, and we are not sure if e.g. we get 1.05 and an uncertainty of 0.2 it is better to have the correction as 1.0+0.25/-0.15, or as 1.05+/-0.2 (as we have now). In any case, the difference in practice is very small: In the region where most of our data is, the uncertainty is small by definition and the smoothing will be close to this value.
- figure 12 left: the average correction seems well below 1. How is that possible if you apply the fake factors derived using the same events and the fit functions match the data points very well in Fig 7? I could expect a shape dependence with mvis but not a correction of the normalisation.
- For mvis~100+/-20, where most of the data is (i.e. determines the average correction), most of the data points are between 0.95 and 1. So it is true that there is a small normalisation shift, however, this can be explained by the imperfect parametrisation (e.g. if you look at the used pt fits, Fig 7, 0 jet & low pt [where most of the data is], the smoothed curve is about 3% below the data points, so this alone could account for most of the difference. But of course there are also other potential sources, like e.g. neglected dependencies.
- Almost all the points of Fig 12 left are 0.90 and 0.95. This means the estimated background in this region is too large compared to the observation. That would be the case if the FF is overestimated. However you say the smoothed curve is too low. Can you check? Also I don’t think neglecting dependencies can lead to a general yield disagreement (but it could make a shape difference) because you are using exactly the same events to derive the FF and to apply it.
- For mvis~100+/-20, where most of the data is (i.e. determines the average correction), most of the data points are between 0.95 and 1. So it is true that there is a small normalisation shift, however, this can be explained by the imperfect parametrisation (e.g. if you look at the used pt fits, Fig 7, 0 jet & low pt [where most of the data is], the smoothed curve is about 3% below the data points, so this alone could account for most of the difference. But of course there are also other potential sources, like e.g. neglected dependencies.
- fig 13: don’t you think a flat line would make as good a job with less overfitting? please check as mentioned already for fig 9. This wavy correction needs to have a physical explanation.
- Note that we only use the left part of the figure, i.e. mTGeV, since this is our SR. In this region, there is a clear systematic shift for mu-tau; for e-tau, it is less clear due to lower statistics, but the central curve is very similar (as it should be, since this is a kinematic effect, not connected to lepton flavor). About the physical explanation: following from the explanation in l248-53, this effect is significant for low mT, in particular mT<30, and still non-negligible up to mT<50 GeV. So the best description of the dependence would likely be some function of mT up to 50 GeV, and then a flat line. This is compatible with what we see and could be implemented - however, since we do not use mT>50 GeV it would not have any practical consequence.
- If you do not care about mT > 50 GeV, you should remove this region from the fit. You should not use any correction that does not look physical even if this is what you obtain from the smoothing. See the answer to the followup on figure 9.
- Note that we only use the left part of the figure, i.e. mTGeV, since this is our SR. In this region, there is a clear systematic shift for mu-tau; for e-tau, it is less clear due to lower statistics, but the central curve is very similar (as it should be, since this is a kinematic effect, not connected to lepton flavor). About the physical explanation: following from the explanation in l248-53, this effect is significant for low mT, in particular mT<30, and still non-negligible up to mT<50 GeV. So the best description of the dependence would likely be some function of mT up to 50 GeV, and then a flat line. This is compatible with what we see and could be implemented - however, since we do not use mT>50 GeV it would not have any practical consequence.
- l293-296: How can you end up with only one uncertainty per final state per jet multiplicity if the fit function has 4(?) degrees of freedom? In principle you should have as many uncertainties per function as the number of degrees of freedom. Otherwise the low pT region for example can constrain the high pT region without any good physical reason. This question applies also to the uncertainties in the corrections; please answer this question for all uncertainties
- It is correct that the a-priori correct way would be to have an uncertainty for each dof. However, as also e.g. with most POG-provided uncertainties, we found that we can sufficiently cover the uncertainties by something similar to grouping them. For the fits, there are actually two uncertainties we use for fits: one alternative shape, normalized to the nominal shape; and the normalization value as norm uncertainty, as described below. This way it is possible for the fit to adjust e.g. just the low pT region, by moving towards the corresponding shape and at the same time to adjust the norm, such that the high-pt region stays ~unchanged, if the fit prefers that. GoF tests and NP pulls and constraints have shown this to work well in past measurements. For corrections, the answer is the same. In addition, there it would be difficult to do it in a different way since the functions are smoothed, not fit.
- What do you mean with "we found that we can sufficiently cover the uncertainties by something similar to grouping them”? Can you show all the 1 sigma alternative shapes for each of these fits to check if these indeed seem to cover all stat uncertainties? How is the alternative shape determined from all these dof?
- It is correct that the a-priori correct way would be to have an uncertainty for each dof. However, as also e.g. with most POG-provided uncertainties, we found that we can sufficiently cover the uncertainties by something similar to grouping them. For the fits, there are actually two uncertainties we use for fits: one alternative shape, normalized to the nominal shape; and the normalization value as norm uncertainty, as described below. This way it is possible for the fit to adjust e.g. just the low pT region, by moving towards the corresponding shape and at the same time to adjust the norm, such that the high-pt region stays ~unchanged, if the fit prefers that. GoF tests and NP pulls and constraints have shown this to work well in past measurements. For corrections, the answer is the same. In addition, there it would be difficult to do it in a different way since the functions are smoothed, not fit.
Comments from Tyler, on Main AN v7:
http://cms.cern.ch/iCMS/jsp/openfile.jsp?tp=draft&files=AN2018_255_v7.pdf
and 2017 presentation related to this correction.
Referring to TopPt reweighting, use-case 3, we've decided to use the Run I reweighting strategy as our analysis-specific reweighting, since we see an improvement by applying this correction (both in 2016 and 2017). Additionally, we have the ttbar category to constrain both the shape and normalization of ttbar further.
- Can a table of contents be added, please?
- I see many great mass plots in the control plots at the end. Can you please try to think of how one could show a mass plot using the ML approach? I know we have talked about biases and sculpting of the shapes in the past but this would still be very interesting to see. What if you did 1 plot each per channel for ggH and qqH categories for reconstructed mass for NN_score > 0.8 or something like that?
- Regarding results (section 12), you only have 4 lines discussing results that are not about impact plots. I don’t see any comparison to the final 2016 HTT cut-based results. You should quote the number in https://arxiv.org/abs/1809.03590 for the ggH + qqH analysis (HIG-16-043) after it was updated for the 2016 combination (WG1 uncertainties and NNLOPS ggH reweighting which you also use). The number is expected mu=1.00 +0.25 -0.23. This number is actually identical to your 2016 combined inclusive expected mu value I see in the table. This is certainly worth mentioning even though the analyses have different uncertainty models as well as ZTT treatment and fake modeling.
-
- Thanks, we will add this. However, are the expected mu numbers also available for the original paper somewhere, plus the mu_VBF and mu_ggH expected values? We could not find them in any of the two papers.
-
- (Tyler:) I do not see the expected values listed in the papers either. I can work with you to help produce these. I think the most relevant comparison is with the 2016 cut-based analysis updated with WG1 and NNLOPS. I have expected mu for this scenario + VH analysis which gives expected mu_ggH = 1.00 +0.48 -0.43, mu_qqH = 1.00 +0.37 -0.39. There is very little ggH and qqH in the VH portion, so this is probably a good proxy for the moment.
- l388, I do not understand this statement: “The transverse momentum of the di-tau plus MET system and in 2016 the transverse momentum of the leading tau as well are required to be larger than 50 GeV respectively in order to suppress QCD multijet background. Both cuts are adopted from the previous analysis”. There was no di-tau plus MET system > 50 GeV cut in the 2016 analysis. Can you clarify? No optimization was checked when using the ML approach to loosen leading tau pT back to what is allowed by the triggers? What is the motivation for the “transverse momentum of the di-tau plus MET system” cut? Can you show a distribution of what this looks like pre-cut? This is essentially Higgs_pT?
- l379, what is your loss in signal acceptance by choosing the isolated pair before trigger matching and extra lepton vetos? Reading the other channels, I do not see trigger matching listed a bit. In MuTau, if the muon only fires the cross trigger, is the tau required to match the tau HLT object? At what point is this enforced?
Investigating impact of making trigger match after pair selection
At first, have a look at events with only one pair (result should be exactly the same for both methods):
Events with fired HLT for one pair: 14795
Events with the the one isolated pair matched to TOs from HLT: 14301
Resulting efficiency: 0.966610341332
Now, consider events with more than one pair
Events with fired HLT and with several pairs: 235
Events with several pairs and TOs from HLT matched to the most isolated pair: 180
Resulting efficiency: 0.765957446809
Dividing out the overall trigger matching efficiency determined for exactly one pair. Resulting (unfolded) efficiency: 0.792415944726
Potential number of events lost by trigger match on most isolated pair: 53.1635687732
As last step, calculating the overall fraction of events being lost by applying the trigger matching on the isolated pair
This fraction is equal or less than 0.0028997256793
Agreed - the information on trigger matching has been added to the AN. The matching happens both for the muon and the tau again for the most isolated pair.
- Figure 15, why are the embedded efficiencies so different? The Elec WP90 high eta plot shows the opposite trend of what is shown in data and other MC. Is the cause understood? Figure 17 also shows differences in measured efficiencies for electrons at the high eta region.
- Figures 21,22, single Mu and single E triggers have much difference efficiency in embedded vs. simulation. Do you know why?
- Section 6.14, I thought ttbar reweighting was no longer recommended in Run-II. Am I wrong? Figure 28, this is after the Z-->mumu mass and transverse momentum reweighting mentioned in section 6.13? Does 2016 show the same improvement?
and 2017 presentation related to this correction.
Referring to TopPt reweighting, use-case 3, we've decided to use the Run I reweighting strategy as our analysis-specific reweighting, since we see an improvement by applying this correction (both in 2016 and 2017). Additionally, we have the ttbar category to constrain both the shape and normalization of ttbar further.
- l991 HTT group should abandon the electron 1% in barrel 2.5% in endcap and adopt EGamma POG recommendations (I am guilty of using this in the past). If not for this analysis, this should certaintly happen for the full Run-II analysis.
- l996 Was there confirmation from JetMET on the JES eta region groupings proposed by Danny?
- l1028 MET Recoil correction uncertainties: Is it correct that these replace the MET unclustered energy uncertainties for the W/Z/Higgs events? Can you quantify the improvement in sensitivity from from this switch? This will help better compare the 2016 cut-based analysis vs. the ML version.
- l1033, top pt reweighting, again I don’t recall this being recommended.
- l1050-1054, I think the Tau POG tracking efficiency is built into the Tau ID SFs. You are measuring a tracking efficiency for "Higher track reconstruction efficiency due to reconstruction in an empty detector environment" and "Migration effects for 1ProngPi0 due to the footprint of replaced muons". Do you apply the normal Tau POG Tau ID SF on top of this? If so, do you know how much double counting may be happening?
- l1096, are you using Barlow-Beeston, or Barlow-Beeston-lite (autoMCStats in Combine 81X)? The Lite version may save you some time on fits, if you are currently using Barlow-Beeston.
- Table 19, there is a degradation of about 10% in expected mu uncertainty comparing the inclusive 2017 mu vs. 2016 mu. Where does this degradation come from considering we had more integrated luminosity in 2017? It looks like the degradation is strongest in tau_h tau_h channel.
- Fig 104, jet eta only go to 4. Do you keep jets up to 4.7? (same for other jet eta plots). Is there cause for worry for the last +/-0.7 in eta?
Comments from Cecile, on FF AN v4/v5:
- Now I understand you take the extremities of the orange bands as shape uncertainties on the fake factors as a function of pT (after normalising because the normalization is another uncertainty). I do not think it is correct. Imagine you fit your functions with a line. In that case the correct way to take the uncertainty into account is to have an uncertainty on the intercept (= normalisation) and one on the slope (=shape). But with your technique you would get the normalisation uncertainty correctly, but the shape uncertainty would have a zero effect (the error band extremities are parallel to the nominal line, which means they are equal to the nominal line after you renormalise them). You completely lose the slope effect. Can you please comment?
- l319 of v4: why is the subtraction of true taus not a shape uncertainty?
- l312: Can you clarify exactly how you derive the uncertainties in the fractions? If you modify the W cross section, all the fractions will change at the same time. How many uncertainties does this correspond to? Also what is the uncertainty on the W (should be more than the cross section because you also have the jet->tauh fake rate uncertainty)?
Comments from Cecile, on NN AN v4:
- l76: seems to be in contradiction with l84 where you say you discard all failing variables and do not do further scrutiny
- l92: what does 6.8 refer to? Is it the total number of expected failing tests and not the number of expected failing tests for m_vis only? It is a bit confusing.
- Fig 4: In practice how do you do the MC subtraction when you train on QCD data events? With negative weights in the training?
- Tab 4: Please add emu. Where is the single top?
- l151: is is standard to validate with training events?
- Appendix B: Why do you do confusion matrices? I understand what they are, but how do you use them? What do you want to check/validate?
- Fig 11 bottom: Is it really useful to have a misc category if the purity is only < 10%?
- Fig 11 center: You have almost as many ggH events in the ggH category as in the zll category.
- Appendix C: I have no idea about how to read these plots and what kind of information they bring. Can you please add some explanation of why they are useful to have in the documentation?
- Appendix D: I cannot understand the difference between the top and bottom plots. And what do you mean by “two on the top” in the caption? I also do not understand how to interpret these numbers (except that the higher the better…)
. It will also referred to in the paper/PAS draft and briefly explained to the detail that a non expert reader can understand the ranking whereever we refer to it. In short, you can see for each output node of the NN, which inputs are the most sensitive ones (to first order). For the figures in appendix D, the absolute values of the first derivative of the respective output node of the NN function is calculated with respect to the inputs. This is performed for each event in the testing dataset and then aggregated by calculating the mean, which is shown in the figures. This means, that zero can be read as “no sensitivity” since the NN response does not change with respect to this variable. However, the absolute value does not have a simple interpretation and it needs to be read relative to the others.
- Fig 23: Why is nbtag not useful to classify in the tt category (at least not more useful than for the other processes)?
- Fig 23: mt_1 seems pretty useless even for w. Is it because of the mt_1<50 requirement applied before? If so it looks to me like this variable could be simply removed (as could be jpt_2, bpt_1, and bpt_2 from this plot)
- Fig 23: jdeta does not seem to be useful to classify in the qqh category
- Most of the above comments apply to Fig 24 too
- Fig 25: Why does m_vis perform better than m_sv for all categories?
- Fig 28: This trend is completely different here where m_sv is much much better. How can you explain this difference between 2016 and 2017?
- Tab 5: As said before, mt_1 does not seem to be anywhere in the useful variables for etau
- Tab 5: in the ggH column, how can you explain that bpt_2:m_vis is ranked so high?
- Tab 6: m_vis is clearly the best variable (before m_sv) in 2016 but was discarded in 2017 because of bad GOF. Wouldn’t it make sense to work more on getting a good data/MC agreement for m_vis to be used in 2017 too?
- In general, can there be overtraining with this method? Have you checked if this happens?
Comments from Martin, on NN AN v5:
- General: Most plots look a bit blurry. Have you tried adding them as pdf instead of png? Not really important if it takes a lot of time, but if it can be done easily...
- General: There are a number of terms which can be left unexplained in a ML journal, but would need a reference (paper) or an explanation (AN) in a physics paper. Not overly urgent, but if you can briefly define/explain in the text some of these terms it would help the average reader a lot. Also, if you have references/explanations (as e.g. for Adam) put them (also, multiple references are fine) where they are first mentioned, or at least forward-reference the explanations.
- 4: Please add an actual reference to the AN.
- 7: for the fits used to measure observables such as signal strength modifiers or cross sections
- 36: Please clarify in the AN whether you first standardize (using events having sensible values) and then assign the default value of -10, or the other way round (i.e. those events are considered in the standardization). It is implictly contained in the text but not very clear, in particular since you described standardization first, and then the default values.
- 47: "manual" optimization is weird, in particular without further explanation. You need not explain your procedure necessarily, but remove the "manual" and add wrt what you optimised (mu? loss? confusion?). Since you use the same value for all channels, I assume you are not very sensitive to the choice, anyway - if that is true, please also state it.
- 74/76: could you make this a bit clearer? First you say that these "bad" variables are not used; and then two lines later that "maybe" they are used after all.
- 87: they -> there
- 104, 109: than -> as
- 106: a suitable simulation sample of this process is not available [since it is not true that there are no QCD samples at all]
- 108ff: this is fine, and just as a long-term comment: You could try to, in addition to inverting the charge requirement, also require e.g. tight isolation, to get a sample purer in QCD. Of course it is not clear if this will improve anything since the "impurities" could also improve the training performance.
- Table 2, 3: emu column is empty (of course you know that...)
- 127: "ground truth" - is that a real word? Either call it "true classes" (plural, as it is a vector) or explain the term.
- 129: "probability...to be found in data" -> "proportional to the rate with which this event is expected to be selected" [it is definitely not a probability]
- 162-164: "dropout layer", "dropout probability" and "L2 penalty" need to be explained.
- 187: I assume "both", i.e. the two matrices per channel, refer to the two folds? This should be made clear here and in the figure captions
- 222: should be -> are
- Fig 11ff: Not urgent, but for the next update, could you make sure the labels are the same as in 18-255 (e.g. QCD, not SS)?
Comments from Isobel, on Main AN v8:
- P.41 Is it expected to have more accurate tau reconstruction in the embedded vs. the MC samples (meaning SF closer to 1)?
- Fig 27. This plot shows good agreement by construction as the ZPT weights are derived based on the ZPT data/MC distribution (correct?) It would be good to validate the agreement in another region.
- P.56 How can the GoF perform well in a high signal region with out biasing the result? One could expect poor data/MC agreement there unless Signal is injected.
- Concerning the impacts, many of them seem different from the 2016 cut based impacts/pulls. It would be good to understand why.
Comments from Cecile, on Main AN v8:
More info on the next 5 bullets here: https://indico.cern.ch/event/762852/#14-sm-htt-20162017-review-comm
- Synchronization on expected and observed limits by category and by data taking period
- Find an algorithm to determine the most optimal binning in each category
- Solve the constraints of the JES by finding a way to smoothen the shifted templates
- Get the scale factors approved by the EGamma and Muon POGs
- Align the cuts between 2016 and 2017 (e.g. there is an unjustified difference in the tautau selection with the tau pT)
- A longer and better justified discussion of all the constrained impacts, and of the reason why the leading impacts are ranked so high
- A discussion comparing the 2016 and 2017 results (strong differences in emu and tautau, which cannot be understood without explanation)
- A discussion comparing this analysis to HIG-16-043
- A more motivated explanation of the different variables (why is it useful?, role of correlations, role of redundancy, …)
- For each single shape uncertainty, illustrate the effect with a chosen process in a chosen category (histograms nominal/up/down)
- All emu plots should be aligned with the others
- All plots/subplots should have a label with the name of the subcategory
- You should show the visible or full mass distributions for all your final plots (instead of plotting the NN output in each category you plot the mass). This is helpful to understand how much including the mass as a training variable biases the shape, and it also overlaps with Tyler’s comment to see a ditau mass distribution for the signal in a category with high signal NN output. I think it could also bring an additional sensitivity, for example in the zll category, where mvis will most likely provide a better discrimination than the NN output, which looks pretty flat. Depending on how the plots look like you can remake the fit and see if using the mass in some/all the categories can improve your results.
- l1066: I don’t think “inexplicably” is the good term…
- l1068 and elsewhere: don’t forget the single top
- l1078: how does the extrapolation of 1.5% give 2%?
- l1080: your explanation of the uncorrelation is in contradiction with what you do for the tau ID (where the discriminators have also changed)
- l1138-1140: I do not understand at all this sentence, and how many nuisances you add at the end
- l1112: are all of them shape uncertainties?
- l1142: why 10%
- l1151: what is the fourth?
- l1157: does that mean you have switched to autoMCstat?
- l1169: why not partially uncorrelated as for the tauh?
- l1193: they should be added in quadrature because they are different objects
- l1211: from the ref it looks like 3% instead. please check
- l1217: I cannot see this number in the reference twiki
- l1232: there should be a correlation by process (I understand the composition is different, but you should just scale the uncertainty per process by the fraction of this process and correlate between categories)
- l1239: the problem does not come from the tools but from the incorrect way with which you define the shape variations (see comments on FF AN too)
- Fig 46: the ditau mass fails the GOF but is included in the input variables
- l1397: this statement is discussable given the number of constraints you still have …
- Sections 12 and 13 need much more explanations and discussions
- l1399: the constraint is most likely artificial and should be resolved
- l1400: the fact that there is a ttbar control region does not explain why the xs uncertainty is constrained and why it is acceptable
- Impacts: why is impact81 split by category? why is the met unclustered energy scale so constrained?
Comments from Cecile, on PAS v0:
Title: This is not the first measurement. ATLAS already has STXS results for HTT.
That's a pity. The title has been changed.
Abstract line 2: I don’t see any measurement of the branching fraction in the result section
The measurement is on cross section times branching fraction. This is what is stated in the abstract.
l62-66: you can remove the details about photons
While in principle yes, photons play a role in the definition of tau had decay modes. A very similar description has been used for HIG-17-020, which went smoothly through the whole publication process and journal review.
l120 and around: CVS -> CSV
Fixed.
l139-147: the WP are different in 2016 and 2017
Added statement that this is for 2016, while for 2017 the WPs have been reoptimized, with a similar performance (no exact numbers, as there is no reference for it).
l163-164: This sentence is strange
Modified to potentially read less strange.
Section 4.2: I do not see where a ttbar-> mu tau_h event would enter in your 3 groups (no fake, but not two taus). Also, l 270, I think embedded samples cover only events with two tau decays, and not with at least one
The events you are referring to would naturally fall into the first event class (l253-l259 electron or muon misinterpreted as originating from a tau decay). L269 has been corrected to refer to event with two genuine tau decays.
l298: There is no mention of the subtraction of prompt contributions
Not sure what you are referring to as "prompt". If you are referring to the subtaction of backgrounds other that the QCD, Wjet and ttbar. This is mentioned in l290f PAS v0.
Table 2: It is not clear which thresholds are for single lepton triggers and which for cross triggers (the tauh cut seems to apply in all cases)
Your observation is correct. The "cross triggers" are used to lower the electron or muon pt thresholds as explained in the text (l313-316 in v0 of the PAS). A clear explanation of what values refer to what triggers is given in the caption of the table.
l360: I thought the tauh pT cuts differed between 2016 and 2017
It was a triviality to harmonize this in the meantime (I think even on a well justified earlier request during review).
Table 3: I do not understand this table and what PPV means
Event classes are introduced in the text (l375ff). On what a positive predictive value in statistics is have a look e.g. here.
l464: You should explain what is meant by mvis vs mvis correlation
Not clear what needs to be explained here. We can add something on the physics interpretation for the reader's convenience.
l 517-538: I had not understood before that you make 2 different fits, one for the inclusive cross sections, and one for the STXS. If this is the case it should be explained clearly in the AN too, and you should justify it. It looks very strange to me to have two different fits since you could obtain both results from a single fit to the STXS categories, and that would most likely also improve your inclusive results. Please check the difference in sensitivity by extracting the inclusive results from the STXS fit.
Indeed the same event category is used for each signal model. The text has been corrected accordingly.
l597: I think 0.8% is not for all decay modes in 2017
Indeed, it is 0.8-1.0%, depending on decay mode -- this is what is already described in the AN and what we are using. Corrected now also in the PAS.
l604: I doubt the tau energy scale still covers the electron energy scale since its prefit uncertainty is much smaller now
Electron energy scale uncertainty has been added.
l637: ZH, WH, and ttH should be part of the inclusive measurement
The formulation has been fixed now to refer only to the stxs stage 0 and 1 measurements.
l633-635: What are these additional uncertainties? I don’t remember seeing that in the AN
This statement has been removed from the text.
Section 8: You should include a table for the definition of the STXS categories instead of the graphs, which do not give precise information and show categories that you do not include.
All available information is part of the figures. More information is given in the text (l549ff in PAS v0). A table cannot add more information and will be more difficult to layout/read.
Section 8: It would be useful to present the STXS results in figures (sigma/sigma_th for all categories)
Agreed the representation of the results is not yet settled. We are happy to work on a proposal towards pre-approval and during ARC review. We will include your suggestions in this proposal.
Section 8 or as additional material: It would be useful to have a matrix that shows the purity of each STXS category in the different STXS signals (see Figure 6 of HIG-18-029 for example)
Agreed. We will add this information during ARC review.
References: Please extend them. At the very least you should include ATLAS 2016 paper, which has STXS measurements, and HIG-16-043, which is the origin of many of the cuts and corrections presented in this PAS.
We have added ATLAS and HIG-16-043 to the references.
.
l464: You should explain what is meant by mvis vs mvis correlation
Not clear what needs to be explained here. We can add something on the physics interpretation for the reader's convenience.
l 517-538: I had not understood before that you make 2 different fits, one for the inclusive cross sections, and one for the STXS. If this is the case it should be explained clearly in the AN too, and you should justify it. It looks very strange to me to have two different fits since you could obtain both results from a single fit to the STXS categories, and that would most likely also improve your inclusive results. Please check the difference in sensitivity by extracting the inclusive results from the STXS fit.
Indeed the same event category is used for each signal model. The text has been corrected accordingly.
l597: I think 0.8% is not for all decay modes in 2017
Indeed, it is 0.8-1.0%, depending on decay mode -- this is what is already described in the AN and what we are using. Corrected now also in the PAS.
l604: I doubt the tau energy scale still covers the electron energy scale since its prefit uncertainty is much smaller now
Electron energy scale uncertainty has been added.
l637: ZH, WH, and ttH should be part of the inclusive measurement
The formulation has been fixed now to refer only to the stxs stage 0 and 1 measurements.
l633-635: What are these additional uncertainties? I don’t remember seeing that in the AN
This statement has been removed from the text.
Section 8: You should include a table for the definition of the STXS categories instead of the graphs, which do not give precise information and show categories that you do not include.
All available information is part of the figures. More information is given in the text (l549ff in PAS v0). A table cannot add more information and will be more difficult to layout/read.
Section 8: It would be useful to present the STXS results in figures (sigma/sigma_th for all categories)
Agreed the representation of the results is not yet settled. We are happy to work on a proposal towards pre-approval and during ARC review. We will include your suggestions in this proposal.
Section 8 or as additional material: It would be useful to have a matrix that shows the purity of each STXS category in the different STXS signals (see Figure 6 of HIG-18-029 for example)
Agreed. We will add this information during ARC review.
References: Please extend them. At the very least you should include ATLAS 2016 paper, which has STXS measurements, and HIG-16-043, which is the origin of many of the cuts and corrections presented in this PAS.
We have added ATLAS and HIG-16-043 to the references.
Comments from HIG-18-032 hypernews link :
Datacards should be checked and approved by the combination group
Done
Fill in the HIG JetMET: https://twiki.cern.ch/twiki/bin/viewauth/CMS/HIGJetMET
Done, reply see below
Include the signal samples : bbH, ggZH, ttH signals, and H \to WW decay. For bbH and ggZH that are not centrally produced estimate the from the ggH and qqZH samples and check with small scale private production
Done (for details, see mail to hypernews)
Slide48 \mu(qqH) -> \mu_V vs. \mu_F
To be done after unblinding with observed data.
The analysis is not sensitive to VH, so the only thing that can be done is tying them to the SM or tying them to another process. Include VH hadronic together with VBF in the plots and explain it in the text and captions/labels.
VH hadronic is now combined with VH. VH leptonic is tyied to the SM expectation
On the way on approval prepare a PR plot to be added in the PAS
We are working on it, will be added before approval.
One of the W+gamma MC samples used is bugged, replace it
Done
Finish your TODO list : synchronization, scale uncertainty
Done
how do you choose the final binning of the DNN distribution?
The DNN score is in the range from 1 / (number of classes) to 1, e.g.,
from 0.2 to 1.0 in the fully hadronic channel. We make the input shapes
for Combine Harvester with a bin width of 0.05 in the sensitive regions
with high neural net score. Finally, we merge bins with no background
contribution from right to left. The full procedure is implemented in
the CombineHarvester dev branch here [0].
why there is some deviation from 1 in the Asimov dataset (e.g. Fig. 3 of PAS)
The definition of our Asimov dataset is signal plus background
expectation. Since we stack only the backgrounds in the upper part of
the plots, the deviation from the Asimov data is equal to the signal
expectation. Same applies for the ratio plot and the deviations from 1.
In principle, it would be better that the optimization samples are
also stat. orthogonal to the ones used for signal extraction. (this is
unclear from reply in sec 3.11)
I'll clarify our approach in the following. We are using a two-fold
approach for the optimization and signal extraction. We divide the full
dataset in half and train the NN on one half and apply it to the other
for the signal extraction. On the second half, we perform this v.v. with
an independent training. Therefore, the events used for the optimization
of the NN are stat. independent to the ones used for the fitting of the
signal.
About the JECs correlation between 2016 and 2017. Can you elaborate a bit more about the value that you are using? Are you aware of this recommendations: https://twiki.cern.ch/twiki/bin/view/CMS/JECUncertaintySources#2016_2017_JEC_uncertainty_correl
we can try to quickly tick off the question about the treatment of JES.
The procedure that we are following had been presented in the JetMET
meeting and blessed for usage for a preliminary result, during the
followup of the JetMET conveners (added in cc to this post, so that they
can confirm).
The JetMET conveners' main concern was about the anticipated treatment
in a legacy combination with other Higgs analyses. This concern is well
appreciated and shared by us, but since we are talking about a PAS and
not the legacy paper it does not apply to the current case. This has
also been the explicit understanding of the JetMET conveners. We will
carefully reevaluated the JetMET uncertainty model for any legacy
results and follow the recommendations given by JetMET, and any eventual
modification that might be agreed upon in a coordinated way within the
Higgs PAG.
About the JER scale factors. Can you elaborate why are they not applied? Are you aware of this recommendations: https://twiki.cern.ch/twiki/bin/view/CMS/JetResolution#JER_Scaling_factors_and_Uncertai
on the JER uncertainties. As Alexei stated in an earlier mail we
inherited and further developed parts of the uncertainty model from the
published HIG-16-043, where these uncertainties were not considered. In
this course in fact it slipped through to check that these uncertainties
can be neglected in face of the impact of the JES uncertainties. This
coincides with our intuition, which is why we took it for given that
such a check had been made in the past. Further investigations so far
seem to indicate that nobody has explicitly checked this up to now. So
we are preparing a quick study to confirm our intuition. We will come
back to answer this question once the results of this study are
available, For a legacy measurement everything will be put back on the table
and we'll take care of better documenting what has been done and what not.
First, one clarification: the stage0 and inclusive fits are done from the same categorized signal regions of stage1, or from fitting the inclusive ggH and qqH signal regions?
They are done from the same categorized signal regions of stage1
For the stage0 and inclusive fit, there is a sizable difference on the 2016 dataset compared to HIG-16-043, especially in the non-VBF categories (mu changes from being ~0.3 sigma above unity to being ~1.5 sigma below unity), possibly from the lepton-tau categories (mu-tau and e-tau both change of ~1 sigma, while e-mu and tau-tau are more stable).
While changes are not unexpected, it may be useful to get a bit more insight on this.
* In this analysis, the 2016 result is from the fit of 2016 alone or from the combined 2016+2017 fit with separate POIs?
The 2016 result is from the 2016 datacards only
* Can you fit separately ggH and qqH in the old cards (HIG-16-043), split by decay mode and combined, in a way that maps better to what shown you have in the current unblinding slides?
For the stage1 fit, it appears that the fit is behaving poorly probably due to the correlation between the parameters (e.g. the qqH vs ggH in the VBF topology bins).
Probably you have to either tie some bins together in the fit or fix them to SM predictions within uncertainties in order to get a more meaningful result. It would be good to see the fit result and the correlation matrix of the POIs in some more restricted scenarios.
Some suggestions could be:
* tie together the pT 120-200 and >200 since you have merged the bins in most final states (and possibly also 1j + 2j?),
or tie together pTH > 200 for 1j and 2j (it will happen anyway in stage 1.1)
* fix the ggH VBF-like events to SM,
or make a single POI for ggH 2j (except possibly pT > 200, or pT > 120)
to be checked
Comments from Andrei, on AN v10 and PAS v2:
The snapshots of PAS v3 and AN v11 are added in this HN post link
The STXS includes ggH, VBF, VH (including gg->ZH), ttH, bbH, tqH (see yellow report), with various sub-bins. While this analysis is clearly targeted to ggH, VBF, VH(hadronic), all other production modes need to be included or shown to have zero contribution. When contribution is not zero, overlap with other dedicated analyses should also be addressed. When you run the fit, it should be clear what is done with each component, if it is floated as signal or background, or fixed. More on this below.
You can find the information on what production modes are considered for the fit and how already in PAS v2 L530-535. We will check whether the information given there is complete and clear enough for everybody to understand what is being done and update the PAS if considered necessary in further versions during the ARC review .
What I see in Tables 2 and 3 (in AN) with MC samples, there are still missing gg->ZH, bbH, tqH MC samples.
As you know there are no official samples available for gg->ZH, bbH nor tqH. The (non-)importance of these production modes for the current analysis has already been studied in detail on privately produced samples or using MSSM bbH Monte Carlo, as a prerequisite to the official pre-approval by the Higgs PAG conveners. As a reminder you can find the discussion posted to the HN here: link . For your convenience we summarize the results of the studies that have been made: i) the effect of gg->ZH is 1,6% of the combination of VBF with all ZH production modes (excluding gg->ZH), with no significant dependency on the stxs stage-1 bins, when compared to the expected sensitivity of the analysis in each corresponding bin; the cross sections of the corresponding production modes have been scaled up inclusively by 1.6%. ii) the effect of bbH is at most 1% of the ggH production cross section (with an expected uncertainty on mu_ggH of 0.2) w/o dependency on the stxs stage-1 bins. The ggH cross section has been scaled up inclusively by 1%.
The production cross section of tqH is roughly one order of magnitude below the ttH production cross (see link). The expectation off ttH events after the baseline selection of the analysis is ≤1 event in basically all event categories. Explicit numbers for the signal categories in the mt channel are ggH: 0.000 (0.003) and qqH: 1.05 (0.99) in 2016 (2017). Note that these are absolute event numbers. The tqH production mode has been neglected for the current analysis. A corresponding clarification has been added to the captions of Tables 2 and 3 and in the text in AN v11. For the legacy paper the importance of these samples for the analysis in the tautau final state will be re-discussed within the HTT subgroup.
I expected to see a table of the relevant contribution of various STXS-1 input modes contributing to different categories adopted in analysis. Just to give you an example from the HZZ pre-approval today, look at slide 23 in [1]. You promised to show explicit numbers for ttH and other such contributions, when we talked on Wednesday.
We will add such a table to the AN and, if considered interesting during the ARC review, also to the PAS.. Maybe with less granularity than presented in the example that you are pointing to, since we see most of the entries empty or plain zero. (What is the difference between empty and plain zero, by the way?). For the expected ttH event yield, see our answer to one of your previous questions.
In the PAS, I see tables 5 and 6, but they are completely empty (why?), and even when those numbers are filled, this would be only partial information. I understand that there is limited space in the PAS, but AN has no such restriction.
These tables have been filled with numbers (after unblinding) in PAS (v3). For completeness the same tables have also been added to AN (v11). Note that tables 5 and 6 in the PAS refer to the 2016 dataset, while the numbers for the 2017 dataset are given in tables 9 and 10. Let us know what you still need as a more complete information for the public. For CMS internal review or more specific questions you are invited to check the complete statistical model that has been uploaded to the corresponding HCG repository (link).
The main result is the fit of mu(qqH) vs mu(ggH), as well as the combined mu. However, from the documentation (Results section in the PAS for example) it is absolutely not clear what is signal, what is background, and what is fixed for H(125). At the HTT meeting you said (please correct me if I am wrong) that ttH is fixed to SM, V(leptonic)H is treated as background, and V(hadronic)H is merged with VBF into the qqH signal. Even if there is not much of a practical difference due to the dominant contribution of ggH and VBF, this needs to be done in a clean way:
(1) spell everything out in the documentation
We appreciate your comment and agree that this should be done in a cleaner way in the documentation. We have added the corresponding clarifications in PAS (v3 L707-716) and AN (v11) (Sections 2 and 12).
(2) when we quote 4.7 sigma H->TT signal, all H(125) contributions should be treated as signal, such as ttH, bbH, tqH, V(leptonic+hadronic)H, VBF, ggH.
The result is the same. A corresponding sentence has been added to PAS (v3 L707-716).
(3) when a 2D scan of mu(qqH) vs mu(ggH) is done, it makes sense to merge all HVV-initiated processes (VBF+VH) into mu_HVV (or mu_V) and all fermion-initiated processes (ggH, bbH, ttH, tqH) into mu_Hff (or mu_f). Can you move to this presentation of results instead of the current Fig.7 content?
This is already what we do, as is stated from the caption of Fig. 7. The axis labels have been changed to make it clear to everybody also from the plot itself (w/o caption) what is being done.
In the HTT report on Wednesday, you had a nice split of fit results between 2016 and 2017, and we discussed that you would approach compatibility comparison with the previous published results on the 2016 dataset.
I do not find any discussion of comparison on the 2016 dataset. To make the proper comparison, one would have to estimate overlap between the two analyses (both in terms of events and in terms of the likelihood fit). This is close to impossible in a practical way (otherwise you would have to run a lot of toy MC with both analyses at the same time). However, it is possible to compare under the two extreme models with 0% and 100% overlap just to see the bounds, and you can also estimate the event overlap in the signal region with the same selection, to get another bound on overlap. However, it is a must to compare to the previous published result in some practical way.
A study of the compatibility between the results of HIG-16-043 and HIG-18-032, including a feasible check of the event overlap is in preparation and will be provided for discussion, soon.
The reported signal is 4.7 sigma. At the HTT meeting you promised to provide plots which should show this unambiguous signal in several ways. There are many plots in AN and PAS, but I do not think you have provided the plots which you promised on Wednesday. I also do not think any of the existing plots illustrate the re-discovered signal with ~5 sigma.
As discussed repeatedly and in many places, such plots will be prepared on the way to approval. We understand that you are eager to see such plots. But we stick to a given order with our work, starting with important items (e.g. related to data understanding) up to presentation items. We think that organizing our work this way makes more sense. As soon as we converge with the ARC on the plots, we will provide them for further discussion to the whole collaboration.
. For your convenience we summarize the results of the studies that have been made: i) the effect of gg->ZH is 1,6% of the combination of VBF with all ZH production modes (excluding gg->ZH), with no significant dependency on the stxs stage-1 bins, when compared to the expected sensitivity of the analysis in each corresponding bin; the cross sections of the corresponding production modes have been scaled up inclusively by 1.6%. ii) the effect of bbH is at most 1% of the ggH production cross section (with an expected uncertainty on mu_ggH of 0.2) w/o dependency on the stxs stage-1 bins. The ggH cross section has been scaled up inclusively by 1%.
The production cross section of tqH is roughly one order of magnitude below the ttH production cross (see link). The expectation off ttH events after the baseline selection of the analysis is ≤1 event in basically all event categories. Explicit numbers for the signal categories in the mt channel are ggH: 0.000 (0.003) and qqH: 1.05 (0.99) in 2016 (2017). Note that these are absolute event numbers. The tqH production mode has been neglected for the current analysis. A corresponding clarification has been added to the captions of Tables 2 and 3 and in the text in AN v11. For the legacy paper the importance of these samples for the analysis in the tautau final state will be re-discussed within the HTT subgroup.
I expected to see a table of the relevant contribution of various STXS-1 input modes contributing to different categories adopted in analysis. Just to give you an example from the HZZ pre-approval today, look at slide 23 in [1]. You promised to show explicit numbers for ttH and other such contributions, when we talked on Wednesday.
We will add such a table to the AN and, if considered interesting during the ARC review, also to the PAS.. Maybe with less granularity than presented in the example that you are pointing to, since we see most of the entries empty or plain zero. (What is the difference between empty and plain zero, by the way?). For the expected ttH event yield, see our answer to one of your previous questions.
In the PAS, I see tables 5 and 6, but they are completely empty (why?), and even when those numbers are filled, this would be only partial information. I understand that there is limited space in the PAS, but AN has no such restriction.
These tables have been filled with numbers (after unblinding) in PAS (v3). For completeness the same tables have also been added to AN (v11). Note that tables 5 and 6 in the PAS refer to the 2016 dataset, while the numbers for the 2017 dataset are given in tables 9 and 10. Let us know what you still need as a more complete information for the public. For CMS internal review or more specific questions you are invited to check the complete statistical model that has been uploaded to the corresponding HCG repository (link).
The main result is the fit of mu(qqH) vs mu(ggH), as well as the combined mu. However, from the documentation (Results section in the PAS for example) it is absolutely not clear what is signal, what is background, and what is fixed for H(125). At the HTT meeting you said (please correct me if I am wrong) that ttH is fixed to SM, V(leptonic)H is treated as background, and V(hadronic)H is merged with VBF into the qqH signal. Even if there is not much of a practical difference due to the dominant contribution of ggH and VBF, this needs to be done in a clean way:
(1) spell everything out in the documentation
We appreciate your comment and agree that this should be done in a cleaner way in the documentation. We have added the corresponding clarifications in PAS (v3 L707-716) and AN (v11) (Sections 2 and 12).
(2) when we quote 4.7 sigma H->TT signal, all H(125) contributions should be treated as signal, such as ttH, bbH, tqH, V(leptonic+hadronic)H, VBF, ggH.
The result is the same. A corresponding sentence has been added to PAS (v3 L707-716).
(3) when a 2D scan of mu(qqH) vs mu(ggH) is done, it makes sense to merge all HVV-initiated processes (VBF+VH) into mu_HVV (or mu_V) and all fermion-initiated processes (ggH, bbH, ttH, tqH) into mu_Hff (or mu_f). Can you move to this presentation of results instead of the current Fig.7 content?
This is already what we do, as is stated from the caption of Fig. 7. The axis labels have been changed to make it clear to everybody also from the plot itself (w/o caption) what is being done.
In the HTT report on Wednesday, you had a nice split of fit results between 2016 and 2017, and we discussed that you would approach compatibility comparison with the previous published results on the 2016 dataset.
I do not find any discussion of comparison on the 2016 dataset. To make the proper comparison, one would have to estimate overlap between the two analyses (both in terms of events and in terms of the likelihood fit). This is close to impossible in a practical way (otherwise you would have to run a lot of toy MC with both analyses at the same time). However, it is possible to compare under the two extreme models with 0% and 100% overlap just to see the bounds, and you can also estimate the event overlap in the signal region with the same selection, to get another bound on overlap. However, it is a must to compare to the previous published result in some practical way.
A study of the compatibility between the results of HIG-16-043 and HIG-18-032, including a feasible check of the event overlap is in preparation and will be provided for discussion, soon.
The reported signal is 4.7 sigma. At the HTT meeting you promised to provide plots which should show this unambiguous signal in several ways. There are many plots in AN and PAS, but I do not think you have provided the plots which you promised on Wednesday. I also do not think any of the existing plots illustrate the re-discovered signal with ~5 sigma.
As discussed repeatedly and in many places, such plots will be prepared on the way to approval. We understand that you are eager to see such plots. But we stick to a given order with our work, starting with important items (e.g. related to data understanding) up to presentation items. We think that organizing our work this way makes more sense. As soon as we converge with the ARC on the plots, we will provide them for further discussion to the whole collaboration.
Comments from the ARC v1, Part I: Physics
Comments on PAS v2, see link.
Answers can be found here.
Comments from the ARC v1, Part II: Text
Comments on PAS v2, see link.
- Line 75-76 (G.G.-C.) Regarding the HLT output, don't write below 1kHz, that's the old run-I value, we were writing above 2-3kHz in the run-II, and even more in 2018 with the B-parking. (G.G.-C.)
- l198: the share -> they share (A.-C.L.)
- l227: it sounds a bit like no genuine pTmiss can be defined for ttbar (A.-C.L.)
- l229: Section3 -> Section 3 (A.-C.L.)
- l236: is the energy scale for electrons misidentified as taus applied for all samples ? how important is that correction ? (A.-C.L.)
- l295-299: maybe good to drop "used for the NN training", it makes it a bit difficult to read, two ideas in one sentence, i.e. the cross check with simulation, and the validity of NN training samples (A.-C.L.)
- l204: the end of the sentence is not clear, "from a DR with 0.2< I_rel^mu< 0.5" (A.-C.L.)
- l372: not clear to me at this stage what are the 3 to 6 background categories (A.-C.L.)
- l377: four NN in total in total for each year --> four NN per year of data-taking (A.-C.L.)
- l426: the superscripts "fit" need to be explained (A.-C.L.)
- l434-447: one could maybe also stress that these tests ensure a robust classification in data (expected to be the same as in simulation) (A.-C.L.)
- l448- 478: interesting but not clear to me if it allows to spot problems or how it is used to cross check the stability of the analysis (A.-C.L.)
- Table 3 and l484 define PPV, remove it from the table. Nevertheless, can you explain more clearly what it is? (G.G.-C.)
- l551-553: POIs probably need to be introduced (A.-C.L.)
- l538-540: One could maybe say here that the information from simulation is used to build templates (A.-C.L.)
- l550: to what refer "event classes" here ? (A.-C.L.)
- l558-559 : "Each event class in this way leads to a dedicated category", same for caption of Figure 6. (A.-C.L.) --> does this mean that the NN output clasification works well and that then one can further categorize ? (Just trying to check if I'm following correctly ;-))
- Figure 5, caption : "with the exception of the >=2 jet classes with H->qq topology". lines 560-561 also mention that it's different for emu etauh and mutauh pTH>200 GeV categories. (A.-C.L.)
- line 565-563: "This is the case... category." (A.-C.L.) -> The NN output naturally leads to an association of such events to the qqH event class. (A.-C.L.) or --> The NN output naturally leads to an association of such events to the qqH categories (A.-C.L.) depicted in Figure 6. ( Else one maybe tries to identify the qqH "inclusive" category on figures 5 and 6.)
- l564 : Don't we have 16 signal categories for tauh tauh ? And then 14+14+14+16+21 = 79 in total per dataset ? (A.-C.L.)
- l715-716 Concerning the quoted cross-sections (G.G.-C.) I don't understand those values. Following the latest HXSWG recommendations, sigmaVBFxBR(Htautau)->3.766*0.06272=0.236pb. Nevertheless, you quote 0.32pb for signal strength 1.03 (?). For ggH, it's almost in agreement, the value is 48.61*0.06272=3.05, while you quote 1.11/0.36=3.08. For the inclusive cross section, does it mean fully inclusive or is it just ggH and VBF. If it's the second one should get (48.61+3.766)*0.06272*0.75=2.46, you quote 2.54. Can you check? And needless to say, can you check you are using the right XS values in the analysis?
Comments from the ARC v2
Comments on PAS v3, see link.
link.
- General: where appropriate (e.g. line 741 and 742)
change qq -> H into VBF + V(qq)H
change gg -> H into gg -> H, bbH, ttH
- Abstract: Change the first sentence. Make it simple and punchy. Not clear what the “They” in the second sentence refers too. You also have “A measurement” which becomes “The measurements” Centre should write “centre” (see discovery paper) Also we are missing a result in the abstract. Suggestion: re-write the abstract. Suggestion:
A measurement of the inclusive cross section $\sigma_{incl}$ for the production of a Higgs (H) boson decaying in a pair of tau leptons, $\beta {\rm H} \rightarrow \tau \tau$, is presented. The measurement is based on the data collected with the CMS experiment in 2016 and 2017 which corresponds to integrated luminosity of 78 fb$^{-1}$ with pp collisions at a centre-of-mass of 13 TeV. A value of $\sigma_{incl} \beta {\rm H} \rightarrow \tau \tau = 2.54 \pm 0.47 {\rm (stat.)} \pm 0.34 {\rm (syst.)}$ pb is obtained. More results in the form of simplified template cross section with a splitting by production mode and kinematic regimes are also provided.
- Line 2-5: No measurement is ever “exact” change to “precise” Also simplify sentence and avoid too many “kinematic properties” in the same paragraph. Suggestion line 3 to 5:
After the discovery of the Higgs (H) boson at [.], one of the main targets of the experiments at the LHC is the precise measurement of the H boson production kinematics, as input to a detailed analysis of its coupling structure.
- Line 12: define tau ($\tau) ounce forever: like the tau ($\tau$) lepton.
- Line 13-14: Not should set in, we don’t know that. Also better merge in one sentence:
The size of these deviations depends on the scale at which new physics could set in as well as on the kinematic properties of the measured process.
- Line 16-36 we cannot use I think the word “former” as it sounds like “obsolete”The results are not obsolete as to what concerns the “discovery” threshold, which we do not want to over-emphasize here in the paper. Also the equivalence Higgs = H as been made in the first sentence of the introduction such that “H” should be used elsewhere. “Higgs” is controversial. “H” is universally accepted. Finally a kind of executive summary of the reason why we present a new (different) analysis method is needed early in the paragraph Suggestion for lines 16 to 35 ([] = your text ):
ATLAS [8] and CMS [9] experiments have previously each reported on the first observation of the H boson in the $\tau\tau$ final state and have provided first cross section measurements. In this paper. In this paper, an inclusive measurement of the product of the cross section for the production of the Higgs boson and the branching fraction for its subsequent decay into tau leptons is presented. Cross section measurements split by production modes and in different kinematic regimes are also determined and presented as simplified template cross sections, as defined by the LHC Higgs Cross Section Working Group [10]. The measurement exploits 78 fb$^{-1}$ of data collected with pp collisions at a centre-of-mass of 13 TeV. The analysis relies on a new method where a differentiation between the individual signal and background sources is performed using a neural net (NN) multi-classification algorithm that allows to maximize the purity of each signal and background process. The NN is used to separate nearly any individual process considered in the analysis of the data into a dedicated event category, based on a fully supervised training. The central element of the analysis [] pair. The $\tau\tau$ final state provides [] (VBF). Tau leptons have a distinct [] Four different finalstates [] decay.Simulated events samples [] method [12].
- Line 181 and 182: Grammar problem, “,” missing plus need re-shuffling. Suggestion: For this purpose, simulated events are used for each individual process except for QCD multijet production.
- Line 183 to 187: Very difficult and long sentence as it is. From what we understood, we try our best to reformulate here:
The training of the NN with respect to QCD multijet events relies on data events selected as described in Section 5 but with the following differences for each final states. In the $e\mu$, [] final states, the selected leptons are requested to carry equal instead of opposite charge. Moreover, the trailing [] is requested to pass the Loose working point and to fail the Tight working point [] otherwise used in the [] final state.
- Line 290: missing “,” -> For this purpose, the number of
- Line 293: avoid yet another “For this purpose”. -> To achieve this, the
- Line 303: avoid yet another “For this purpose”. Suggestion: For this objective, all corrections
- Line 466-496 : one should consider shrinking this part.
- Line 669: not clear what is meant with “A dedicated uncertainty scheme”
- Figure 7 bottom: please proceed to the merging of some of the multijet bins as discussed at the ARC-Author meeting.
- Provide the details of how the NN output "probabilities" are constructed, in particular write explicitly that the "probabilities" are the NN targets assuming equal yields from each of the signal and background species.
- Maybe add a sentence on the fact that linear correlations between NN outputs have been investigated and very good agreement is observed between the predictions and the data.
- Try to add some additional information/caveats on the systematic uncertainty bands shown on the plots along the lines of what Roger explained during the meeting (neighbouring bin correlations, eventually some info on the bkg normalisations uncertainties, etc)
After the selection step, the events are further split into two signal-like and several background-like categories. These categories are to be distinguished from the event classes introduced to indicate the hypothesized truth. An event class usually coincides with a single The sentence has been rephrased according to the suggestion
Comments from the Anne-Catherine on PAS v5
- l26: "purity" -> "selection purity" (?)
Has been changed to “The analysis relies on a new method where a differentiation between the individual signal and background sources is performed using a neural net (NN) multi-classification algorithm that allows establishing several categories very pure in the respective signal or background process.”
- l28: "supervised training" is mentioned in several places in the document, to be skipped it some places.
Has been removed at L407: The training of the NN is performed based on…
- l26-27: "The NN... training" -> "The NN is used to separate nearly any individual process considered in the analysis and to assign the events to dedicated categories."
Changed to “The NN is used to separate nearly any individual process considered in the analysis by assigning the events to dedicated event categories, based on a fully supervised training.”
- l87: What is meant by "for further processing" ?
Can be removed
- l91: Not so clear if tracks are inputs to jets or to the primary vertex.
Rephrased to “The physics objects for this purpose are the jets, clustered using the jet finding algorithm [21, 22] and the associated missing transverse momentum. Hereby, jets are build using all charged tracks associated with the vertex, including tracks from lepton candidates and missing transverse momentum is taken as the negative vector sum of the p T of those jets.”
- l95: "purity" -> "selection purity" ?
Has been changed to “To reduce the number of particles wrongly identified as electrons...”
- l98: "to identify electrons" can probably be removed, can be guessed from the previous sentences.
Has been changed to “For this analysis working points with an identification efficiency between 80 and 90% are used.”
- l119-120: re-optimised version -> optimised version or simply "(b jets) the combined secondary vertex b tagging algorithm (CSV v2) and a NN..." Some readers may start to wonder if the dependency to the data taking period is related to the choice of the discriminant or to the optimisation of CSV.
Changed to “an optimized version”
- l128-130: One should probably specify that all tau decay modes are considered, including 3pi and pi+2pi0s.
We think that this is already specified. The text says : ”For the analysis the decay into three charged hadrons and the decay into a single charged hadron with up to two neutral pions with pT > 2.5GeV are used.”
- Footnote from Table 1:"Decays into genuine tau leptons taken from EMB." ->"Decays into genuine tau leptons taken from embedded samples."
Done
- l171: "Drell-Yan production in the subsequent decay into tau leptons" -> "Drell-Yan production in tautau final states" Or something easier to read.
Changed to “The most prominent background process in the eμ, eτ h and μτ h final states is Drell–Yan production of Z \rightarrow \tau\tau”
- l177-178: Maybe a bit vague. "Similar arguments...(diboson)." ->"Similarly, single top-quarks and vector boson pair production (diboson) contribute to the etauh and mutauh final states."
Done
- l243-245: "For the statistical analysis (...) Section 5.4." -> "The overall normalization of this background is constrained for the relevant final states by ttbar categories, that reaches purities of >= 90%, as decribed in Section 5.4." To make the sentence shorter. "expected purity" -> it doesn't probaly matter here how this purity is computed (on simulation).
Changed to “The overall normalization of this background is constrained for the relevant final states by dedicated $\ttbar$ categories, which reach purities of ${\gtrsim90\%}$, as described in Section~\ref{sec:Event_categorization}.”
- l248: "whenever simulation is used" can probably be skipped.
Yes, we agree. We removed it from the sentence.
- l250: One can probably skipp "for the signal extraction". -> "The backgrounds, described in Section 6, can be misinterpreted as signal in three ways:"
Yes, we agree. We changed the sentence to “All backgrounds can be misinterpreted as signal in three ways:”
- l261-62: What kind of selection is used to select Z->mumu ? a quite loose preselection ? looser than the ones from the analysis ?
The selection is tight enough to ensure a high purity of genuine µµ events and at the same time loose enough to minimize biases of the embedded event samples. The selection of the muons defines the minimal selection requirements to be used in the analysis plus a requirement on the invariant mass of the muon pair to ensure that Z → μμ events are selected predominantly.
- l273: "via the reuse" -> "by considering" (?)
Changed to “This is achieved by reusing the full data set of selected \mu\mu events for each final state.”
- l274-276: "It has been checked that the overlap of the resulting embedded event samples is small enough such that the distributions that are related to the part of the event that originates from the observed data are uncorrelated." -> "It has been checked that the overlap of the resulting embedded samples is small enough such that the distributions related to the part of the event originating from the (actual ?) data are uncorrelated."
Changed to "It has been checked that the overlap of the resulting embedded samples is small enough such that the distributions related to the part of the event originating from collision data are uncorrelated."
- l323: "for the analysis" can probably be removed.
Done
- l389-390: The first sentence says the same as the second one, to be removed (?)
Yes, we agree. The first sentence has been removed.
- l392: "to indicate the hypothesized truth" -> "to describe the origin of events" (?) Maybe I'm wrong but "hypothesized truth" sounds complicated
Replaced with “These categories are to be distinguished from the event classes that correspond to the true origin of the processes.”
- l402:"to order events by expected purity" -> "to assign events to categories with high purity" Can one define an "event purity" ?
Has been reformulated to “This probability allows to order events in a given category by expected purity from low to high NN output.“
- l407: "based" -> "using"
Done
- l411: "exception of a miscellaneous (misc) event class for each final state" -> "exception of miscellaneous (misc) event classes" "for each final sate" can probably be dropped as everything is well explained in the next sentence.
Done.
- l418: "The training is split in two folds." -> not clear why, because of two data periods ?
No, we divide the samples into two independent sets for training. In a first step, one step is used for training and the other set for the evaluation. Afterwards, the set used for training before is used for evaluation and the former evaluation set is used for training. We change the sentence in the PAS to: “For each final state and data taking period two fold cross validation is used for training” We hope that this is clearer.
- l454: "20(18)" -> according to the table it's "20(17)" (?)
Yes, that is true. Thanks for spotting this.
- l489: What is meant with "predominantly", all events at the end should lie in a given category.
Changed wording to “This procedure ensures that each event class is predominantly collected in the dedicated event category.”
- l562: "and a total of 9 PIOs" can probably be skipped.
Yes, we removed it.
Comments from the ARC on PAS v6
Introduction
- Line 2: There are 6 « Higgs » and one floating « H » in the intro. Introduce at first occurence « Higgs (H) »
Done.
- Line 5, 9, 12 change « Higgs boson » to « H boson »
Done.
- NDLR: Please propagate everywhere Higgs bosons -> H boson
Done.
- Line 8: Higgs sector is slang. Change to « in the SM scalar sector »
Done.
- Line 10: I guess you mean the standard wording « . to Brout-Englert-Higgs mechanism »
Done.
- Line 32: The following sentence does not look right grammatically: « . separated well from the overwhelmingly large background comprised of jets produced . » Maybe you meant «. composed of jets »
I’ve picked this sentence from the CMS publication guidelines. But I will cross-check with John’s comments.
- Line 36 to 40: It is unclear why the section 4 comes after section 5 in the list. I fail to find what is wrong with the natural ordering in the text with sections 2,3,4,5, .
This was a bug and has been changed for v7 already. Sorry that we have bothered you with this.
- Lines 105 and 106: Suggestion to make it simpler _« The contributions . backgrounds to the electron or muon selection are further reduced .
I don’t understand the suggestion you make to simplify, but I’ve moved “contributionS” from singular to plural as you suggest.
- Line 113: Just a comma missing « To mitigate any distorsions from PU, only those . »
Done.
- Line 137-139: Just a (needed) comma missing _« To distinguish ... quarks or gluons, a multivariate . is used
Done.
- Line 214-217: This sentence is very difficult, maybe saved by (important here) missing « , » Suggestion: « For the comparison . data, corrections are derived . trigger paths, for . efficiency, and in the efficiency . »
Done. I’ve added commas, where suggested.
- Line 232: Here appears for the first and only time « Zjets » Change to « Z + jets events »
Done. This was also a bug that has been fixed for v7 already. Thanx for spotting.
- Line 288: You write « All processes given in Table 1 typically contribute to more than one of these groups. » Are you here referring to the « three ways » introduced with the previous bullets ??? How does Z -> tau tau contribute « to more than one » group ??? Isn't it only the second group ?? Suggestion for line 250: « All backgrounds in Table 1 can be misinterpreted in different ways. There can be grouped as in the following. »
Done. Thanks for this suggestions.
- Line 317: Suggestion: « Same-charge and opposite-charge transfer factors . »
This sentenc has sligthly changed since v7.
- Line 333: A (needed here) « , » If the events passed . of the triggers, the lepton identified
Done.
- Table 3: Change in caption (since you will re-use the labels later in Fig. 1): « All event classes (labelled below ggH, qqH, ztt, QCD, tt, misc, zll, wj, db and st) enter the training . »
Done.
- Line 462: Just a (needed) comma missing: _« . between the input parameters, those are
Done.
- Line 474: Suggestion: Here a « Thus » fits better: « Thus, it is expected that . » or « It is thus expected that »
Done.
- Line 495-496: Suggestion « Given the uniform prevalence, the PPV . »
This section has changed in v7.
- Line 635 and 636: You refer to section 4.1 but What you call « reweighting method » here was called « weighing » in line 241 of section 4.1 . weighing or reweighing ??? Chose.
Changed to weighting
- Line 681: A (needed here) « , » _« In turn, a significant
DONE
- Line 749: the present tense « are » looks strange . Suggestion: « . have been investigated in terms . »
Changed
Comments from the LE on PAS v7
general
- I don't think we should say "\tau\tau pair" anywhere. It's either (for example) a "\tau\tau final state" or a "tau pair". It might seem to some people that a "\tau\tau pair" is four taus!
Changed to "tau pair"
- I think the title needs work. It sounds like we are measuring something very general - the Higgs boson production cross section (which has several components) - but in the tau tau final state, which is one of the decay modes. So it's a bit schizophrenic. I suggest: "Measurement of Higgs boson production and decay to the \tau\tau final state" More discussion might be in order.
Changed. Thanks for the suggestion."
- Second sentence: "The measurement is based on pp collision data collected by the CMS experiment in 2016 and 2017 corresponding to an integrated luminosity of $77.4~\text{fb}^{-1}$ at a center-of-mass energy of $13~\text{TeV}$."
Changed"
- Last sentence: "Results are also presented in terms of cross sections for individual production modes and kinematic regimes."
Changed"
- line 3+ "targets" -> "goals"; "as input so a detailed analysis of its coupling structure" -> "in order to elucidate its coupling structure."
Changed"
- line 7 "On the other hand, " (comma)
Done"
- line 12 Remove "especially"; change "like" -> "such as". Physics-wise this statement applies in Type II 2HDM; I am not sure we should call the tau a "down-type fermion".
Added changes"
- line 13 What scale? Energy? Mass?
Written “energy scale” and added “This scale could be set by the mass of one or more additional heavy Higgs bosons.” in L14
- line 16 "experimental sensitivity"
Added experimental"
- line 27 "multiclassification" or simply "multiclass"; no hyphen in either case. Perhaps "classification" without "multi" is even better: after all, if you are classifying there are multiple classes.
Changed to classification
- line 85 "LHC run periods"; move "on average" to the end.
DONE
- line 90 Specify R for anti kt jets?
Added: “FASTJET [21,22] and discussed below, and the associated...”
- line 91 Suggest dropping "Hereby"
Dropped
- line 96 "wrongly" -> "incorrectly"
Done
- line 104 "For this analysis, ..." (comma)
Done
- line 174 "...followed by that from Z -> \tau \tau."
Done
- line 179 This paragraph is not making sense to me. The opening sentence promises "two approaches" but it is far from clear what the different approaches are; only one overall approach is described in the rest of the paragraph.
Removed first sentence
- line 184 "...relies on observed events..." I would begin the next two sentences with "Firstly, ..." and "Secondly, ..." respectively.
Done
- line 195 "The production samples of...."
Changed to “For simulation of the diboson production processes...”
- line 230 "...are determined from observed $\ttbar$ events (for true $\cPqb$ jets) and observed $\Zjets$ events (for misidentified light quark and gluon jets)."
Changed
- line 239 "Correction factors accounting for deficiencies in the modeling of Drell-Yan $\Pe\Pe$, $\Pgm\Pgm$ and $\Pgt\Pgt$ final states are determined from the ratio of observed to simulated $\ZMM$ events in bins of $\pt(\Pgm\Pgm)$ and $m(\Pgm\Pgm)$."
Replaced
- line 242 "...to better match the observed top quark p_t distribution [43]."
Done
- line 249 Not making sense...if it is a background it must look like the signal! I think what you meant is "The various processes leading to background in our signal selection arise in a number of ways:"
Thanks. Changed
- line 251 I think you mean "...misinterpreted as originating from a $\Pgt$ decay to hadrons." This is crucial for understanding the rest of the paragraph.
Changed
- line 259 I suggest the following rewrite: "The number and kinematic distribution of these events are estimated using the $\Pgt$-embedding technique described in Ref. [11], which uses observed $\ZMM$ events in which the energy deposits and charged tracks associated with the muons are removed and replaced with those from simulated $\Pgt$ decays. In that way, hybrid events..." I think a good deal of the rest of the paragraph could be dropped, as all this is discussed in Ref. 11.
Changed sentence and removed L266-L271 and the last sentence
- line 279 "Especially" -> "In particular, events..."
Changed
- line 290 In eq. 5, the left side has an index i and the right side does not
Added
- line 295 "differentially"....hmmmm. Bin bin-by-bin or is there a smoothed shape/function? Clarify.
Changed to: “The $\FF^{i}$ are determined differentially as a smooth function of the $\pt$ of the $\Pgth$ candidate”
- line 317 "Events are selected online during pp collision running using different trigger requirements."
Changed
- line 329 "If an event passes only one of the triggers, the lepton..."
Changed
- line 344 "Finally, to distinguish..." (comma)
Done
- line 345 "...final state, all events..." (comma)
Done
- line 406 "For the training, these..."
Done
- line 409 "run period"
Done
- line 410ff I am wondering if the entire paragraph can be reduced to a single sentence describing a NN setup and training which is standard in some way, described elsewhere, and can be a reference. This is almost a minitutorial…
This is intentional since it is the first time this approach is used in HTT. It is targeted at readers not familiar with the concepts of machine learning
- line 449 Suggest "All NN input parameter distributions are tested for the level of accuracy with which they are described by the statistical model used for signal extraction, in each final state."
Thanks. Changed
- line 457 "Because the NN analysis gains statistical power compared with a cut-based analysis by utilizing the correlations among the input parameter distributions, these correlations are examined in addition to the marginal distributions of each parameter."
Changed
- line 465 comma after "approach"
Changed
- line 514 "The statistical uncertainty in the observed number of events..."
DONE
- line 518 "The ratio of the observed number of events to the expectation ..."
DONE
- line 520 comma after "signal"
DONE
- line 529 comma after "model"
DONE
- line 569 "limited statistics"
DONE
- line 574 I would not mind a reference to my 2011 paper somewhere around here:
Added reference
@Article{Conway-PhyStat,
author = "Conway, J. S.",
title = "Nuisance Parameters in Likelihoods for Multisource Spectra",
journal = "Proceedings of PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN, Geneva, Switzerland, 17-20 January 2011, edited by H.B. Propser and L. Lyons",
volume = {CERN-2011-006},
pages = 115-120,
url = "http://cdsweb.cern.ch/record/1306523/files/CERN-2011-006.pdf",
year = "2011"}
- line 576 "as uncorrelated"
DONE
- line 584 "triggers used"
DONE
- line 610 "as correlated"
DONE
- line 626 "as correlated" (check this everywhere)
Checked and changed
- line 669 "In turn, ..."
DONE
- line 703 Somewhere in this paragraph you need to mention the priors assumed on the nuisance parameters.
Added in L700: “... fit to the data. For all normalization uncertainties $\mathcal{C}_{j}(\hat{\theta}_{j}|\theta_{j})$ is chosen to be a lognormal distribution. Shape altering uncertainties are implemented using a vertical morphing algorithm. The uncertainties may...”
- Generally, everywhere we have a label such as Z -> ll, please use the script \ell, done in Root by using \\ell\\ell.
\ell is broken in TLatex and can not be used as label unfortunately
- Figs. 1-2. It is too late to properly capitalize the row/column axis labels for each category in Fig. 1, and the titles in Fig. 2?
The labels correspond to the class/category names and are therefore not capitalized
- Figs. 2-4, 13-23. The colored bars for the signal vanish when viewing the document at even 125% on my screen. What can we do to avoid this?
Increased width of signal
- Table 7 The capitalization of row and column titles needs to be fixed here.
DONE
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