Search for chargino and stop (2LOS) with Run2 data


Comments to AN-19-256 v2:

The content in the AN-19-256 referred to in the following answers has been implemented in v3.

Email from Jae Hyeok Yoo (25/01/2021):

3 Physics objects event reconstruction
  • L 201: pt>20 GeV. Have you checked we have no PileUp jets with this. Has PileUp JetID been applied? This is most relevant for low ptmiss bins.
    • We are applying loose PU jet ID (we added the information in the AN v3). We basically only use events with ptmiss>100 GeV.
  • L252-253: Are these numbers only for 2018 or all years combined?
    • Those numbers are only for 2018. We clarified this in the AN v3.

4 Search strategy

  • L270-272: Why is the veto selection so high in pt. Any 3l bkg leaking in will cause problems. So maybe it is better to use a lower pt cut on the veto lepton?
    • Indeed, we select veto leptons requiring pT>10 GeV, not pT>15 GeV. We corrected it in the AN v3. The pT>15 GeV is the requirement we apply on the analysis (“tight”) leptons when we pre-select them at the time of producing our trees, hence the typo in the AN.
  • Table 1: Here you are using MET>140 GeV while MET>160 GeV is used for CR definitions. 140 GeV is typo?
    • Table 1 shows our baseline selection before the optimisation of the search regions (which is described in Section 4.1). This baseline selection is essentially what was used in the 2016 paper. In Section 4.1 we describe the further studies done to optimise the search regions for the legacy analysis, which led to the choice of MET>160 GeV. We tried to make this point more clear in the AN v3.
  • L316: how is average calculated, weighted by cross section or assuming the same cross section?
    • We assume the same cross section, in order not to bias the optimisation towards lower mass regions.
  • L 322: I assume you split this non-interesting region in so many bins to help your fit. It would be useful to show this with a fit plot.
    • Yes, as the top and WW backgrounds are normalised in the low MT2 region in the fit itself, we split this region in more bins to take advantage of different MT2 shapes among the backgrounds. This might also help to constrain the nuisances for shape systematics.
    • We are not sure we can provide a fit plot at this stage, as the analysis is still blinded. We tested the effect of merging the low MT2 bins on the expected chargino exclusion limits for the SUS-17-010 paper, and found some improvement at low mass splitting.

5 Background estimation

  • You check the modeling of MT2 in regions inclusive in Njets. Have you checked the modeling for Njets=0 and >=1 separately?
    • We added this check in AN v3. Instead of Figure 7, showing the MT2 distributions in events with no b-tagged jets, we put two figures where those events are split in Njets=0 and Njets>=1.
  • L358: You are saying that you test ttbar, tW, and WW backgrounds, but the contribution from tW is still small in these CRs. So I am not sure how its modeling can be tested.
    • In this section, we study the modeling of MT2 for backgrounds with two W bosons. The concern here is that for these processes the MT2 distribution has a kinematic endpoint at the W boson mass, so events enter the high MT2 signal region mainly because of detector resolution effects.
    • We study therefore dedicated control regions to check how well the simulation models the tails of the MT2 distributions against jet mismeasurement and lepton misidentification.
    • It is true that the contribution of the tW events is always rather small with respect to the of one of ttbar production, but we just assume here that these two processes would be affected in a similar way by the detector resolution effects of concern, as their final states are rather similar.
    • Finally, as the proportion of ttbar and tW remains similar going from the CR to the SR, a validation of the overall top (tW+ttbar) MT2 shape seems to be enough.
  • L 375-379: can we show MC closure of this method. Also, this cannot work perfectly since there is some differences between W and Z bosons, so is there an uncertainty associated with this big grin
    • We guess that by closing you mean a comparison between the MT2 distributions in WW and WZ (with WW emulation) events. It is certainly true that the two distributions cannot match (we made this study for the 2016 paper, see for instance the third bullet here).
    • The point is that we are not assuming them to be identical, as we are not using this method for estimating the shapes of the background from processes with two W bosons in the signal regions. These shapes are taken from the simulations.
    • We use this method only for validation purposes, in particular to check the MT2 modeling in high MET regions, which can be populated by a larger fraction of events with jet mismeasurement. The idea is to complement the check we do in the validation region with 100<MET<140 GeV, which is good to check the general modeling of MT2, but cannot prove the highest MET regions.
    • So in short, the only assumption of this check is that the possible mis-modeling of the detector resolution effects on the MT2 shape does not depend on the difference between W and Z bosons.
  • L420: How is MT2 calculated with 3 leptons?
    • We use just two leptons to compute the MT2. The plot in Figure 8 in AN2019-256-v2 is done by reconstructing a candidate Z boson by the pair of same flavour oppositely-charged leptons with closest invariant mass to the Z boson mass, and computing the MT2 by using the third lepton and the lepton in the Z boson pair with opposite charge respect to it . For completeness, we added in AN2019-256-v3 the plot obtained by using the Z boson lepton pair to compute the MT2.
  • L434-437: If this is for testing WZ, please move it to 5.2.1.
    • This is for testing the ZZ background. We changed “As for the WZ production,” to “Just as we do for the WZ production,”
  • L471: Discrepancy is still 50% level even without the smearing, so I wouldn’t say that “mismodeling is largely covered … ”.
    • What we mean by “covered” is that the mismodelling is smaller than the variation observed when removing the JER smearing, as the data/MC ratio moves from 0.3 to 1.5 (so it is 50%, but “on the other side”). We tried to make it more clear in the AN v3.

7 Observed results

  • WZ production: so you are focusing on the 3l final state and showing the shape works there. But if I look at your results tables, then there is a strong difference between DF and SF for WZ background. This seems to indicate that we usually do not see the W boson. Can you check this and also see why we are not seeing this. Are we sure we do not have any W decaying hadronically and then mismeasurements here as well. Or tau decays...
    • We studied the composition of the WZ background in the search region (plot here) in terms of the origin of the two reconstructed leptons. Events with a lepton from the W boson are the majority and are symmetric between DF and SF channels. The difference between the channels in the tables appears at high MT2 because of the contribution of events where both the reconstructed leptons come from the Z boson decay. Indeed, the MT2 for these events does not have a kinematic endpoint (as is the case for events where one lepton comes from the W), and can acquire large values when the dilepton pair is back-to-back with the neutrino from the W boson decay.
  • For the non-prompt leptons I wanted to understand why there are no W+jets in your regions.
    • We observed in the 2016 analysis that the contribution from W+jets was negligible. We added this process in the same-sign regions for AN v3. Its contribution is found to range between 2 and 5% of the expected SM background through the data taking years.
  • In general, I am wondering what you do with your cross-checks in the CRs. It seems that you just verify that the MC looks fine, but do not assign any systematic uncertainty to this. Is this correct? I am wondering whether we want to consider using statistical uncertainty on the cross-check since this is the level we have checked the MC.
  • Just out of curiosity. Did you ever look at some other variables (e.g. the angle between the leptons) to see whether there is anything that could give some extra discriminating power between WW and the signal, since even at very high MT2 we have a considerable amount of WW left
    • Yes, we did look at other variables, especially in the first iteration of the analysis (razor variables, angular variables, sum of object transverse momenta).

Comments from 27 November 2020 presentation ( Link to the slides in Indico):

The content in the AN-19-256 referred to in the following answers has been implemented in v2.

  • Which triggers are used in the analysis?
    • Tables summarizing the trigger paths used in the analysis have been included in the Appendix B of AN-19-256.

  • Same-sign control region (slide 12): would it be possible to include this control region in the fit and let it decide the value of the scale factors for the rate of nonprompt leptons?

    • This is not of easy implementation as the nonprompt lepton scale factors affect a component of each background rather than a specific background as a whole. As a first approach, we evaluate the impact of the nonprompt scale factors by repeating the fit using a non-prompt SF = 1 +/- measured deviation in the same-sign control region. The test is done for the T2tt and the TChipmSlepSnu models. In each plot, the black line shows the (blinded) exclusion region obtained from the new fit, while the red one shows the original result obtained by setting the nonprompt SF to the value measured in the same-sign control region. Given the very small impact of the nonprompt lepton SFs to the fit, we think that the integration of the same-sign control regions in the fit would be an overkill.

  • Drell-Yan mismodeling in 2017 (slide 17): what is the contribution of this background to the search regions? How much does its mismodeling affect the fit?

    • The yields for Drell-Yan production and other background processes in the search regions are compared in Section 7 of AN-19-256. For 2017 (page 32), Drell-Yan production is one of the main contributors in the lower ptmiss regions and mt2 bins, becoming increasingly less relevant at higher ptmiss and mt2 values.
    • To evaluate the impact of the 2017 Drell-Yan mismodeling, we repeat the fit by using Drell-Yan estimates before JER smearing. No significant change is found in the (blinded) exclusion regions neither for the T2tt or the TChipmSlepSnu models (as before, black lines refer to the new fit with no JER smearing for the Drell-Yan background, while red lines refer to the original fit).

  • For the normalization of WZ, ZZ, and ttZ production, you measure global scale factors in suitable control regions with ptmiss>160 GeV and use them in all the search regions (slides 14 to 16). Is there any dependence of these scale factors on the ptmiss and jet multiplicity bins used to define the search regions?

    • We compare observed and expected yields as a function of ptmiss and jet multiplicity in the control regions used to study the WZ, ZZ, and ttZ backgrounds in Sections 5.2.1, 5.2.2, and 5.2.3 of AN-19-256, respectively. No significant trend is observed.
    • We modify the fit used to extract the signal in our analysis, by adding the WZ, ZZ, and ttZ control regions to it, and letting the fit itself to determine the normalization of these processes in each ptmiss and jet multiplicity bin (for ttZ production, only ptmiss bins are considered, as the corresponding control region is defined by requiring at least one b-tagged jet; this process is anyway relevant only in search regions with b-tagged jets). The new approach yields very similar (blinded) exclusion regions for the T2tt and the TChipmSlepSnu models (as before, black lines refer to the new fit with WZ, ZZ, and ttZ control regions, while red lines refer to the original fit).

-- PabloMatorrasCuevas - 2020-12-14

Topic attachments
I Attachment History Action Size Date Who Comment
PNGpng CharginoSignalRegionsSmearNoDYOptimPtmMT2HighExtraEENoiseDPhiHEM_vs_CharginoSignalRegionsSmearOptimPtmMT2HighExtraEENoiseDPhiHEM_TChipmSlepSnu_mC-100to1400_Blind_Contours_2016-2017-2018.png r1 manage 26.7 K 2021-01-05 - 10:27 LucaScodellaro  
PNGpng CharginoSignalRegionsSmearOptimPtmMT2HighExtrabinFitCREENoiseDPhiHEM_vs_CharginoSignalRegionsSmearOptimPtmMT2HighExtrabinEENoiseDPhiHEM_TChipmSlepSnu_mC-100to1500_Blind_Contours_2016-2017-2018.png r1 manage 29.1 K 2021-01-05 - 10:27 LucaScodellaro  
PNGpng CharginoSignalRegionsSmearnonpromptSFOptimPtmMT2HighExtraEENoiseDPhiHEM_vs_CharginoSignalRegionsSmearOptimPtmMT2HighExtraEENoiseDPhiHEM_TChipmSlepSnu_mC-100to1500_Blind_Contours_2016-2017-2018.png r1 manage 28.4 K 2021-01-05 - 10:27 LucaScodellaro  
PNGpng StopSignalRegionsOptimisedPtmAndMT2ISRSmearEENoiseDPhiHEMFitCR_vs_StopSignalRegionsOptimisedPtmAndMT2ISRSmearEENoiseDPhiHEM_T2tt_mS-200to800_Blind_Contours_2016-2017-2018.png r1 manage 29.1 K 2021-01-05 - 10:27 LucaScodellaro  
PNGpng StopSignalRegionsOptimisedPtmAndMT2ISRSmearEENoiseDPhiHEM_mismodel_vs_StopSignalRegionsOptimisedPtmAndMT2ISRSmearEENoiseDPhiHEM_T2tt_mS-200to800_Blind_Contours_2016-2017-2018.png r1 manage 29.8 K 2021-01-05 - 10:27 LucaScodellaro  
PNGpng StopSignalRegionsOptimisedPtmAndMT2ISRSmearEENoiseDPhiHEM_nonpromptSF_vs_StopSignalRegionsOptimisedPtmAndMT2ISRSmearEENoiseDPhiHEM_T2tt_mS-200to800_Blind_Contours_2016-2017-2018.png r1 manage 30.0 K 2021-01-05 - 10:27 LucaScodellaro  
PNGpng TChiSlepSnu_7BinsVs4Bins_contours.png r1 manage 12.3 K 2021-07-29 - 14:23 LucaScodellaro  
PNGpng WZComposition_mt2ll.png r1 manage 9.5 K 2021-04-16 - 10:31 LucaScodellaro  
PNGpng log_cratio_WZ_3Lep_ZLeps_ptmiss-160_mt2llOptimHighExtra.png r1 manage 29.7 K 2021-04-16 - 10:21 LucaScodellaro  
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