NumericalLikelihood

Numerical Likelihood calculation with toy sampling

In this section we are going to show the methods and tools that were developed in order to sample our likelihood with toy samples. A method has been developed to generate toy MC with different lifetime values from the initial signal MC which we know it was generated with a value of 1.511 psec lifetime.

Toy Generation

As explained in the introduction to perform the following measurement toy samples need to be generated on a large scale. For this reason we need to generate toy MC with different lifetime values. Since, doing this on a large scale will require a lot of time, what we decided to do is to start from the signal MC we have from the 2015/2016 analysis and with the use of the sampling method try to generate sample with different lifetime values. After the generation then the standard bootstrap method is going to be used to generate full toy sets (sig+bkg) that are going to be used to fit the data sPlots for sampling our likelihood. The main reason why the accept/reject method has been used is because we want to include also the truth proper time, measured proper time and invariant mass potential correlations.

Accept/Reject sampling cross-checks

In this section we are showing the plots that are cross checks on our sample method. We start by using the exponential distribution of truth proper time in our sample that we know it has a lifetime value 1.511 psec. Then we calculate a distribution with the target lifetime. In the sampling method what is required to identify the relative scale of the two functions the supremum of the Target/Original is required to be found in the given range. In this way you are making sure that the random number generated between [0,1) is going to properly accept or reject the event. In the following plot we tests for different lifetime values what is the number of events accepted every time.

SignalDatasetToy SamplingMethod NumberofEvents DifferentLifetime.png
Number of events accepted for different lifetime values, with a lifetime step of 0.1 starting from 1.0 and ending to 3.0
SignalDatasetToy SamplingMethod TimeRequiredPerSampling.png
Time required for sampling every time the singal MC with a different lifetime value, the plot has been generated for the same range as for the number of events plot
SignalDatasetToy SamplingMethod PullsResidualsOfValidationFitOnTruthTime.png
For validation we fit part of the exponential distribution to check that the lifetime value is correctly set.
SignalDatasetToy SamplingMethod MassProperTimePlotsForDifferenteLifetime.png
Invariant mass, proper time, truth proper time samples for different lifetime values

After generating datasets with different lifetime values, we are using the standard bootstrap method to generate the full datasets in order to use sPlot and create the signal distribution for the fit.

FullDatasetToy 1000 Lifetime01 sPlot.png
sPlot example distribution for a random toy generated with lifetime 0.1 ps and bootstraps from the bbmumuX for bkg

Truth time fits

In order to investigate the problematic look of the pulls and residuals for the accept/reject samples with different lifetime values, we generated signal only toys with bootstraps for a given lifetime value and fitted the same region as above ([0,12] ps is the fit range):

SignalDatasetToy 1000 LifetimeValue10 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.0 ps
SignalDatasetToy 1000 LifetimeValue11 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.1 ps
SignalDatasetToy 1000 LifetimeValue12 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.2 ps
SignalDatasetToy 1000 LifetimeValue13 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.3 ps
SignalDatasetToy 1000 LifetimeValue14 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.4 ps
SignalDatasetToy 1000 LifetimeValue15 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.5 ps
SignalDatasetToy 1000 LifetimeValue16 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.6 ps
SignalDatasetToy 1000 LifetimeValue17 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.7 ps
SignalDatasetToy 1000 LifetimeValue18 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.8 ps
SignalDatasetToy 1000 LifetimeValue19 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 1.9 ps
SignalDatasetToy 1000 LifetimeValue20 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.0 ps
SignalDatasetToy 1000 LifetimeValue21 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.1 ps
SignalDatasetToy 1000 LifetimeValue22 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.2 ps
SignalDatasetToy 1000 LifetimeValue23 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.3 ps
SignalDatasetToy 1000 LifetimeValue24 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.4 ps
SignalDatasetToy 1000 LifetimeValue25 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.5 ps
SignalDatasetToy 1000 LifetimeValue26 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.6 ps
SignalDatasetToy 1000 LifetimeValue27 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.7 ps
SignalDatasetToy 1000 LifetimeValue28 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.8 ps
SignalDatasetToy 1000 LifetimeValue29 PullsResiduals.png
Pulls residuals of truth lifetime fit with generated lifetime = 2.9 ps

Fit per fit investigation

Values that are smaller than 1.511 which is the actual generation value seem to have a better fit performance than the values that are greater than it. In the following we see some example fits

Fit Range Generation Value Fit Result Residual Value Pull Value Plot
[2.5,15] 1.10 ps 1.10296 +/- 0.0105497 ps 0.00296 0.2805767
SignalDatasetToy AcceptReject GenValue1.10ps FittingRange2.5 15.0.png
Fit of truth proper time generated with a different lifetime value with accept/reject. The fit range is attempted to be far from the turn on part of the distribution
[2.5,15] 1.73 ps 1.69709 +/- 0.0162986 ps -0.03291 -2.0195
SignalDatasetToy AcceptReject GenValue1.73ps FittingRange2.5 15.0.png
Fit of truth proper time generated with a different lifetime value with accept/reject. The fit range is attempted to be far from the turn on part of the distribution

there are features that can be seen in both plots. For example we see that the values at larger lifetime ~15ps produce a reduced pull distribution which has a worst performance. Next attempt is to see whether by reducing this range we can obtain a better performance.

Fit Range Generation Value Fit Result Residual Value Pull Value Plot
[2.5,12] 1.10 ps 1.10453 +/- 0.010629 ps 0.00453 0.42619249
SignalDatasetToy AcceptReject GenValue1.10ps FittingRange2.5 12.0.png
Fit of truth proper time generated with a different lifetime value with accept/reject. The fit range is attempted to be far from the turn on part of the distribution
[2.5,12] 1.73 ps 1.72597 +/- 0.0174094 ps -0.00403 -0.23148414
SignalDatasetToy AcceptReject GenValue1.73ps FittingRange2.5 12.0.png
Fit of truth proper time generated with a different lifetime value with accept/reject. The fit range is attempted to be far from the turn on part of the distribution

Varying lower edge of fit no BDT cut

For identifying the best fitting range to cross check the accept/reject method what happened is that we removed all cuts applied in the sample (BDT, reconstructed time) the following configuration has been applied:

BDT cut Reco Time Cut Lower edge range Upper edge value Binning Step Gen Value Chi2 Cut
No No [0,7]ps 15ps 30 0.5ps 1.533ps BinContent > 10

with the above configuration the following fitted times were obtained and the corresponding p-values excluding the bins with less than 10 events for a more accurate calculation

FittedTimeAndPValue BDTCut-999LowerEdge1 Range0 7 GenValue1.533.png
Varying lower edge from 0,7 with 0.5 ps step and upper edge 15 ps

Varying upper edge of fit no BDT cut

The next step was to start varying the upper edge to identify a combination of lower and upper edge values that provide a good fit result. For this reason the following two configurations have been applied:

Configuration BDT Cut Reco time cut Lower edge value Upper edge range Binning Step Gen Value Chi2 Cut
1 No No 0.5ps [8,15]ps 30 0.5ps 1.533ps BinContent > 10
2 No No 4.0ps [8,15]ps 30 0.5ps 1.533ps BinContent > 10

the reason why we are including two configurations for the upper edge is the following. The first configuration has as a lower edge the smallest possible value that fits the correct lifetime, whereas in the second configuration we include also an acceptable p-value for the fit from the lower edge. As a cutoff of acceptable p-value we use the limit 0.05, therefore the value 4ps has been chosen. By applying the two configuration we obtain the following two plots:

Configuration 1 Configuration 2
FittedTimeAndPValue BDTCut-999LowerEdge0 Range0.5 15 GenValue1.533.png
Varying upper edge from 8,15 with 0.5 ps and lower edge 0.5ps
FittedTimeAndPValue BDTCut-999LowerEdge0 Range4 15 GenValue1.533.png
Varying upper edge from 8,15 with 0.5ps and lower edge 4ps

Troubleshooting (NEW)

Several issues have been identified and needed to be checked in order to draw a valid conclusion for the fits performed above.

Fit step issue

The fits with lower edge [0,4]ps show a remarkable constant behavior. By looking at the fit logs it seemed that the fits were failing, for this reason a decision was made to setup the following configuration:

FitRange BDT Cut Reco time cut LowerEdge UpperEdge Binning EdgeStep GenValue Chi2Cut FitErrorStep FitParLimits
LowerEdge No No [0,7]ps 15ps 30 0.5ps 1.533ps BinContent > 10 10 [-50,50]
UpperEdge No No 0.5ps [8,15]ps 30 0.5ps 1.533ps BinContent > 10 10 [-50,50]

With the configuration stated above the following fit has been obtained

TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.533NewErrorStepDefinitionNoRebinningAdded.png
Fit with new fit steps, and changing lower edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.533NewErrorStepDefinitionNoRebinningAdded.png
Fit with new fit steps and changing upper edge

Double fitting with New Range

The next step was to perform a double fitting with the new step to check if this is showing any different fit result. Specially for the first bin where we see a value that is far off from the other ones. The configuration from the table above is still valid.

TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.533DoubleFittingAppliedNewErrorStepDefinitionNoRebinningAdded.png
Fit 2x with new fit steps and changing lower edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.533DoubleFittingAppliedNewErrorStepDefinitionNoRebinningAdded.png
Fit 2x with new fit steps and changing upper edge

Double fitting with Old Range

The method of double fitting in order to obtain a better fit instead of what is quoted in the initial studies, and without having to tune the fitter with a new step on the fit parameter has bin tried also with the old range and the following configuration:

FitRange BDT Cut Reco time cut LowerEdge UpperEdge Binning EdgeStep GenValue Chi2Cut FitErrorStep FitParLimits
LowerEdge No No [0,7]ps 15ps 30 0.5ps 1.533ps BinContent > 10 2e+05 [-1e+06,1e+06]
UpperEdge No No 0.5ps [8,15]ps 30 0.5ps 1.533ps BinContent > 10 2e+05 [-1e+06,1e+06]

with the following results:

TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.533DoubleFittingAppliedOldErrorStepDefinitionNoRebinningAdded.png
Fit 2x with the old fit steps changing the lower edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.533DoubleFittingAppliedOldErrorStepDefinitionNoRebinningAdded.png
Fit 2x with the old fit steps changing the upper edge

In both cases we see that there is no improvement in bin 1 where we see a very different fitted value. However in both tests we see up to know that 1.533ps does not seem to be the correct fitted value.

New generation value

After checking in the job option of the MC generation we identified that the value used for the B0s to decay is 1.4727ps instead of 1.533ps quoted in the thesis for this reason we repeated the tests with the single fit and the old step with the new value

FitRange BDT Cut Reco time cut LowerEdge UpperEdge Binning EdgeStep GenValue Chi2Cut FitErrorStep FitParLimits
LowerEdge No No [0,7]ps 15ps 30 0.5ps 1.4727ps BinContent > 10 2e+05 [-1e+06,1e+06]
UpperEdge 1 No No 0.5ps [8,15]ps 30 0.5ps 1.4727ps BinContent > 10 2e+05 [-1e+06,1e+06]
UpperEdge 2 No No 6ps [9,15]ps 30 0.5ps 1.4727ps BinContent > 10 2e+05 [-1e+06,1e+06]

those configuration were applied with a single fit step and the following results were obtained

TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded.png
Fit with old steps and changing lower edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded 0.5psLowerEdge.png
Fit with old steps and changing upper edge with lower edge at 0.5ps
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded 6psLowerEdge.png
Fit with old steps and changing upper edge with lower edge at 6.0ps

it seems that we are able to reproduce the same result as above without having to worry about the fit steps.

Removing cuts which affect the truth proper time

On the studies shown above it is clear that independent from the starting value the truth time fit which contains the first bin [0,0.5]ps shows no improvement even when a different starting point or calling the fitter twice is applied. This indicates that Bin 0 in the fit process is affected by selection biases that were not removed. For this reason in the section all the selection cuts, either at derivation or at ntupling level are listed in the following table:

Derivation Cuts Ntupling Cuts Affecting truth time Correlation plots
  Combined muons No
VariableCorrelationWithTruthTime.png
Correlation between truth time and variables affecting it
  pT1) > 6 GeV and pT2) > 4 GeV No
  Abs(η(μ)) < 2.5 No
  pTB > 8.0 GeV Yes
  Abs(ηB) < 2.5 No
  χ2B/NDF < 6 Yes ?
  m(B) (4766,5966)MeV No
  DRflight < 1.5 Yes
  Abs(a2D) < 1.0 Yes
  Lxy > 0 Yes

following to the list of cuts stated above the following triggers are used:

Trigger Year Total Lumi Prescaled
HLT_mu6_mu4_bBmumu 2015 3.93 No
HLT_mu6_mu4_bBmumu 2016 20.36 Yes
HLT_mu6_mu4_bBmumu_Lxy0 2016 26.03 Yes

With listing the set of cuts that are applied it is going to be easier to identify all the quontities that affect the truth proper time. Removing those cuts from the analysis ntuples will give the possibility to improve also the first bin in the studies to identify an optimal range for fitting the truth time.

Removing cuts and fitting Bin 0 of the histogram (Full Range)

In the following section an evaluation of the cuts applied to the data sample is going to be shown. The cuts listed in the table were applied recursively one by one and the sample was fitted in the full defined range [0,15]ps. For producing the plot below the fitted value with the respected error was taken from the unbinned fit and plotted againt the cut number from the table to indicate which cut was used each time.

Cut Number Cut Name
0 No Cut
1 BpT
2 Bχ2
3 Lxy
4 a2D
5 DR
6 Trigger
7 Only Lxy Trigger
8 Only non-Lxy Trigger

the fitted values obtained when fitting with the inclusion of one cut at a time on the full dataset, odd :

CutAdditionProcessAndFittingHistograms.png
Fitted values when adding one by one the cuts listed above full dataset
CutAdditionProcessAndFittingHistograms OddEvents.png
Fitted values when adding one by one the cuts listed above on odd datas
CutAdditionProcessAndFittingHistograms EvenEvents.png
Fitted values when adding one by one the cuts listed above on even data

Fits without a specific trigger

In the following section the fits performed with only one of the two triggers active at a time (which fitted value is quoted above) are shown for comparison:

TruthTimeFits BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0 15psNoRebinningAddedLxyTriggerOnly From0.0to15.0.png
[[https://twiki.cern.ch/twiki/pub/Main/NumericalLikelihood/FitLogLxy0Trigger.txt][FitLogLxy0]
TruthTimeFits BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0 15psNoRebinningAddedNonLxyTriggerOnly From0.0to15.0.png
[[https://twiki.cern.ch/twiki/pub/Main/NumericalLikelihood/FitLogNonLxy0Trigger.txt][FitLogNonLxy0]

Change lower edge of fitting range on sample without the cuts

In this section the lower edge of the fitting range has been changed as in the procedure described above (from 0.0 to 7.0 with a step respecting the bin width wich is 0.5). Additionally the same scan was run on the odd and even samples in order to check for possible statistical fluctuations

Full sample Odd events Even events Plot to compare (biased sample)
TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.4727NoRebinningAdded.png
Fitting full sample without any truth time related cut
TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeOddDataGenValue1.4727NoRebinningAdded.png
Fitting odd sample without any truth time related cut
TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeEvenDataGenValue1.4727NoRebinningAdded.png
Fitting even sample without any truth time related cut
TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded.png
Fit with old steps and changing lower edge

Change upper edge of fitting range on sample without the cuts

In this section we perform the same procedure described above but instead of varying the lower edge of the fit we fix it for some values and then we change the lower edge in a range [8,15]ps with 0.5ps step.

Full sample Odd events sample Even events sample LowerEdge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.4727NoRebinningAdded Lower0.0ps.png
Fitting full sample without any truth time related cuts and changing the upper edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeOddDataGenValue1.4727NoRebinningAdded Lower0.0ps.png
Fitting odd sample without any truth time related cuts and changing the upper edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeEvenDataGenValue1.4727NoRebinningAdded Lower0.0ps.png
Fitting even sample without any truth time related cuts and changing the upper edge
0.0ps
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeGenValue1.4727NoRebinningAdded Lower0.5ps.png
Fitting full sample without any truth time related cuts and chaning the upper edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeOddDataGenValue1.4727NoRebinningAdded Lower0.5ps.png
Fitting odd sample without any truth time related cuts and changing the upper edge
TruthTimeFitspValuePlot BDTCut-999ChangeUpperEdgeEvenDataGenValue1.4727NoRebinningAdded Lower0.5ps.png
Fitting even sample without any truth time related cuts and changing the upper edge
0.5ps

Removing Lxy, a2D, DR, Trigger cut

As a first step all the cuts affecting the lifetime have been removed. The order of applying the cuts is the following Lxy, a2D, DR and Trigger. The following configurations has been applied for our fit study:

FitRange BDT Cut Reco Time Cut Lxy Cut a2D Cut DR Cut Lower Edge Upper Edge Binning Edge Step Gen Value χ2 cut Fit step
Lower Edge No No No No No [0,7]ps 15ps 30 0.5ps 1.4727ps Bin Content > 10 2e+05

with the following results and an example fit of the first bin:

TruthTimeFitspValuePlot BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAddedNoLxyTriggerNoCutFile.png
Varying lower edge of the fit with no ntuple level cut applied and upper edge 15
TruthTimeFits BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAddedNoLxyTriggerNoCutFile From0.0to15.0.png
First bin fit with the configuration shown in the tableFitLog

Fitting range that includes Bin0 in the histogram (0-0.5)ps

In this section the fit on the range [0,15]ps will be presented with the cuts applied shown in the table above. We are attempting to fix the fitting by applying a second fit directly after the first fit and check whether the issue is due to the initial error step being extremely large:

the second attempt to fix the problem was to change the error step manually (change the range of the variable) and use the new step in a single step:

It is clear that the reason of having the first fit with the larger error step is due to initializing the fitter with a larger step and making it difficult to identify the correct position of the minimum

Varying lower edge of fit with BDT cut

The same procedure defined above has been applied in this scenario but this time the BDT cut of 0.3 has been applied in the sample to introduce the turn on effect that is present in all our plots up to now. The following configuration has been applied as described in the table:

BDT cut Reco Time Cut Lower edge range Upper edge value Binning Step Gen Value Chi2 Cut
0.3 No [0,7]ps 15ps 30 0.5ps 1.533ps BinContent > 10

with the above configuration applied the following fitted times were obtained with the corresponding p-values

FittedTimeAndPValue BDTCut0.3LowerEdge1 Range0 7 GenValue1.533.png
Varying lower edge from 0,7 with 0.5 ps step and upper edge 15 ps

Varying upper edge of fit with BDT cut

After checking the lower edge fits now the next step again was to apply the same procedure and same configurations for the upper edge. Again two configuration have been tried where one is with the lowest possible value and the second one the value that gives a correct fit and an acceptable p-value.

Configuration BDT Cut Reco time cut Lower edge value Upper edge range Binning Step Gen Value Chi2 Cut
1 0.3 No 0.5ps [8,15]ps 30 0.5ps 1.533ps BinContent > 10
2 0.3 No 4.0ps [8,15]ps 30 0.5ps 1.533ps BinContent > 10

with the above two configuration applied the following plots have been obtained

Configuration 1 Configuration 2
FittedTimeAndPValue BDTCut0.3LowerEdge0 Range0.5 15 GenValue1.533.png
Varying upper edge from 8,15 with 0.5 ps and lower edge 0.5ps
FittedTimeAndPValue BDTCut0.3LowerEdge0 Range4 15 GenValue1.533.png
Varying upper edge from 8,15 with 0.5ps and lower edge 4ps

it can be seen clearly that the fits with the lower edge of 0.5ps are dominated by the BDT effect and they provide a very bad p-value to our fits.

Fitting unbiased truth only sample

For finilizating the study of the proper time value in our sample an unbiased truth only sample has been used which was generated with the same job option but with looser criteria on some of the generation variables (will be added soon). The sample consists of 1M events and with the use of the AthenaProduction 20.20.7.2 ntuples were created helding the information to calculate the truth proper time variable. After the sample was created the next step was to perform the fitting as it is described in the precious studies where we are changing the lower edge of the fit by the size of a bit and check the fitted value.

TruthSample ChangingLowerEdge.png
Fitted values in unbiased sample when chaning the lower edge in a range [0,7] with a step 0.5ps

Accept/Reject validation

The validation of the accept/reject method is essential for understanding that the toy generation for our final fit is under control and well understood. Many steps are entering the calculation of the new PDF from an existing one for this reason an attempt to split and check all the steps has been taken. Initially the pull/residual plot has been generated for the full method to check whether there is any bias. Following to the outcome the decision of which checks need to be made has been taken

AcceptRejectToys 100 WithFixedNumberofEvents.png
Accept/Reject toys with a fixed number of events per toy

Validation of maximum of PDF ratio identification

For identifying the maximum of the ratio from two distributions a schematic approach has been employed and validated with Mathematica.

Mathematica ROOT
MathematicaPlotForMaximum.png
Mathematica plot for identifying the maximum of the PDF ratio
ROOTPlotForMaximum.png
ROOT plot for identifying the maximum of the PDF ratio

Random number generation

Another step entering the calculation of the accept/reject method is the generation of randon number based on which the decision of keeping or discarding the event is taken. For this reason for a single plot we plotted the random number generation in a histogram to see it's uniform behavior

RandomVariableGenerationPlot.png
Random number generation for a single accept/reject toy

Target PDF toy generation

Another component entering the Accept/Reject method is the PDF function that we want to go to. For this reason toys were generated from the target PDF and fitted with the same model as the one fitted in the accept/reject toys. The number of events was allowed to fluctuate based on a Poisson distribution with mean the value of the entries in the signal MC sample used.

AcceptRejectTargetPDFToys 100 WithNumberOfEventsFromPoissonWithMeanFromSignalMC.png
Accept/Reject target PDF toys with number of events according to poisson with mean the entries from signal MC

Fit Function Toy generation

In the process of producing the toys for accept/reject an extended maximum likelihood fit is being applied on the unbinned data set. The fit region of the fit is what has been studied above and applied in the toy generation [0.5,15.0]ps. A generation of 100 toys has been applied with the number of events in each set to be set on the number of events in the signal MC.

FitFunctionToys 100 WithNumberOfEventsFromPoissonWithMeanFromSignalMC.png
Fit function toys for validation of the fit model with number of events taken from fit singal MC entries

Accept/Reject toys for various lifetime values

In this section a set of toy shave been generated witht he accept/reject method in the range [1.00ps,3.00ps] with a step of 0.25ps in order to validate the the toy generation.

Fit Sample Fit range Lifetime range Toys Lifetime step
Truth proper time Signal MC without lifetime cuts [0.5,15]ps [1,3]ps 1000 0.25ps

in order to void listing all the pull/residual plots what was prepared is a signle plot for the mean and std. dev. for all the different lifetime values and the corresponding errors

MeanSigmaPullResHistograms.png
Mean/Std.Dev. from pull and residual plot for different lifetime value generated with accept/reject

Full dataset generation

The introduction to sample generation is going to be explained in this section. The procedure for generating the toys for a given lifetime value is the following:

  1. Load singal MC and bkg MC files
  2. If needed sample from the singal MC to generate a new lifetime value rather than the generation value
  3. Mix bkg and signal with the bootrstap method according to the expected number of events from the data fit
  4. Fit generated samples with full background + signal models and obtain sWeights
  5. Produce sPlot projections for each of the toy sets
  6. For each toy weight the signal distribution with different lifetime values

Dataset generation

The first towards the numerical approach of the chi2 fitter is to validate the toy generation for signal + background samples. The bootstrap method has been selected as stated above, with the following configuration:

BDT cut Number of toys Expected signal events Expected background events Poisson fluctuation Plots for 10 first toys
0.3 10 50 590 true Toy samples histograms
0.365 10 49 152 true Toy samples histograms

the following step on generation is to fit the sample and evaluate the performance of the invariant mass fitter since with the use of this fitter we are going to discriminate singal and background in the decay time distribution (sPlot). The fitter has been applied with the following configuration:

BDT cut Combinatorial starting point SSSV starting point Signal starting point Fit logs Fit result plots
0.3 (-0.05,200) (500,100) (5366,60,60) Fit log Fit result on 10 toys
0.365 (-0.05,50) (500,50) (5366,60,60) Fit log Fit result on 10 toys

applying the sPlot procedure on the generated toys and the models described above lead to the following plots. Additionally it was decided to apply the different mass models also for the whole sPlot toys procedure.

BDT cut Model Range sPlot and fit plots
0.365 Cheb 1st order + Exponential + Gaussian [4766,5966] MeV Fit plots and sPlots
0.365 Cheb 0th order + Exponential + Gaussian [4766,5966] MeV Fit plot and sPlots
0.365 Cheb 1st order + Gaussian [5200,5966] MeV Fit plot and sPlots
0.365 Cheb 0th order + Gaussian [5200,5966] MeV Fit plot and sPlots
0.365 Exponential + Gaussian [5200,5966] MeV Fit plot and sPlots

Study on parameters entering chi2 metric fit (toy sample)

Fitting the sPlot distribution with the MC templates using a chi2 metric proved to show poor results in pulls and residuals. The main effect observed in the pulls is that the estimation of the error on the fit parameter was heavily biased. For this reason we the sampling approach has been employed to identify if all the parameters entering the chi2 metric calculation for each bin are properly estimated. The first parameter that is essential is the content of each bin. For this reason we generated with the procedure described above 1000 toys according to the expected number of events for the background and the signal allowing the statistics to flactuate according to a Poisson distribution. Only fit's that succeed in convergence were propagated in the estimation of the bin content obtained from the sPlot. In addition for each of the bins we projected the distribution of the contents on the y-axis to obtain the distribution of the contents. In the projection plots what is visible additionally, is with a red line the value of the MC template, and with a green line the mean value of the distribution.

Model Range Parameter/Number of ct bins 8 12 24 Reference distributions for toys Summary plots
Cheb1 + Expo + Gaus [4766,5966]MeV mean mean plot 8 bins mean plot 12 bins mean plot 24 bins Bin content distribution for full toy sample Bin 5 summary
sigma sigma plot 8 bins sigma plot 12 bins sigma plot 24 bins Bin content distribution for signal events in full toys  
Cheb0 + Expo + Gaus [4766,5966]MeV mean mean plot 8 bins mean plot 12 bins mean plot 24 bins Bin distribution for bkg events in full toys  
sigma sigma plot 8 bins sigma plot 12 bins sigma plot 24 bins Error distribution for full toys  
Cheb1 + Gaus [5200,5966]MeV mean mean plot 8 bins mean plot 12 bins mean plot 24 bins Error distribution for signal events in full toys  
sigma sigma plot 8 bins simga plot 12 bins sigma plot 24 bins Error distribution for bkg events in full toys  
Cheb0 + Gaus [5200,5966]MeV mean mean plot 8 bins mean plot 12 bins mean plot 24 bins    
sigma sigma plot 8 bins sigma plot 12 bins sigma plot 24 bins    
Expo + Gaus [5200,5966]MeV mean mean plot 8 bins mean plot 12 bins mean plot 24 bins    
sigma sigma plot 8 bins sigma plot 12 bins sigma plot for 24 bins    

what is importat in each of the plots above is to compare the bin content with the complete content (unweighted) expected on average in each bin. Since, those distributions are for the signal projections obtained from sPlot we know that they are weighted. In this case with the unweighted distribution comparison we will be able to gauge the bgk rejection due to sPlot, and see if the distributions are suffering from the fact that the mass fit does not separate the signal and background correctly. For this reason three toy distributions are shown below with the event separation from signal and bakcground as created in generation

toyGeneration BDT0.365 PropTimeDistWithSignalAndBkgComponents 8ctBins.png
Toy sample 1 with BDT 0.365
toyGeneration BDT0.365 PropTimeDistWithSignalAndBkgComponents 8ctBins 2Toy.png
Toy sample 2 with BDT 0.365
toyGeneration BDT0.365 PropTimeDistWithSignalAndBkgComponents 8ctBins 100Toy.png
Toy sample 3 with BDT 0.365

the average entry witht he average error per entry in the toys can be seen in the two following plots:

toyGeneration BDT0.365 AvgMeanWithAvgSigma 1000Toys.png
Average bin content with average error in each bin
toyGeneration BDT0.365 AvgMeanWithAvgSigma 1000Toys OnlyLastBings.png
Zoomed in last bins

Weight distribution per sPlot bin on toys

The above presented plots with the central value and the sigma of the sPlot toys, show some structures which cannot be directly understood. For this reason the following plot are being produced, where we are plotting for each toy all the weights along with the the identification whether it's coming from signal or background (since those are toys samples from the full singal MC and bkg MC).

Model Range Binnning Plots Summary graphs
Cheb1 + Exp + Gaus [4766,5966]MeV 8 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
sPlotToys BDT0.365 WeightMeanDistributionForDifferentMassModels.png
Weight mean value for signal events
Cheb0 + Exp + Gaus [4766,5966]MeV 8 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
Cheb1 + Gaus [5200,5966] MeV 8 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
sPlotOnToys BDT0.365 WeightScatterPlotsForAllModels bkgEvents.png
Weight mean value for bkg events
Cheb0 + Gaus [5200,5966] MeV 8 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7

an effect seen for all background and region models, is that the last bin in lifetime contains always 0 background. The source of the effect is the very low number of background events in the sample due to the BDT cut. By plotting the unweighted proper time distribution for BDT greater than 0.365 we can see that bin 8 contains 0 events (Proper time distribution for background).

Change of signal fit model

A source leading to failures in the toy fitting along with mis behavior of the sPlot technique is the choice of the signal PDF in the mass fit. Up to know a single gaussian fit has been choosen which can be seen in the following plot in 10x the statistcs expected in the signal region does not provide a sufficient description for the data. For this reason an improvement has been employed which is the use of a double gaussian model where the sigma of the two gaussians and the fraction are being fixed to the values obtained from the MC (as a first attempt of improvement). The fit to the signal MC shows that the double gaussian is a more accurate model and for this reason preferred instead of the single gaussian.

NewSignalMassModelVsOld.png
Double gaussian fit vs single gaussian for the Bs signal

The fit result for the double gaussian fit vs the signle gaussian can be summarized in the following table:

Model Parameter name Initial values Final values
Single gaussian mean 5366 5346.27
sigma 160 103.66
NoE 4672 4672
Double gaussian mean 5366 5350.40
sigma 1 60 82.1985
sigma 2 160 177.637
fraction 0.5 0.836496
NoE 4672 4672

From the plot shown above with the pull distributions of each model we can see that clearly the performance of the double gaussian with the common mean is better and therefore used to repeat the studies shown above. The model used was the same double gaussian with fixed the parameters of the sigma and the fraction. Essentially the only parameters fitted in the toy samples are the mean and the normalization

sPlots for toy fits with new signal model

The sPlot obtained on the same toys as above are being shown in the following table:

Model Plot Successfull new signal model Successfull old signal model
Cheb1 + Exp + Double gaussian 12 sPlots 982 875
Cheb0 + Exp + Double gaussian 12 sPlots 998 963
Cheb1 + Double gaussian 12 sPlots 947 916
Cheb0 + Double gaussian 12 sPlots 1000 977
Exp + Double gaussian 12 sPlots 877 843*

* Some of the fits are failing but the fit status remains 0 so there is not reliable way to exclude those fits

Bin distributions for different ct bins

The next step after validating the sPlot procedure is to produce the bin distribution plots along with the error distribution for each of the bins. The main idea is to see if by improving the mass fit we can also improve the separation of different sources. This is expected to show an improvement in the low entry bin distributions were the multi-peak structure can be seen. One of the potential reasons of this multiple peak structure is the leak of background events in tthe signal distribution.

Model Distribution ct bins
Cheb1 + Exp + Double gaussian mean Bin content for 8 ct bins Bin content for 12 ct bins Bin content for 24 ct bins
sigma Error for 8 ct bins Error for 12 ct bins Error for 24 ct bins
Cheb0 + Exp + Double gaussian mean Bin content for 8 ct bins Bin content for 12 ct bins Bin content for 24 ct bins
sigma Error for 8 ct bins Error for 12 ct bins Error for 24 ct bins
Cheb1 + Double gaussian mean Bin content for 8 ct bins Bin content for 12 ct bins Bin content for 24 ct bins
sigma Error for 8 ct bins Error for 12 ct bins Error for 24 ct bins
Cheb0 + Double gaussian mean Bin content for 8 ct bins Bin content for 12 ct bins Bin content for 24 ct bins
sigma Error for 8 ct bins Error for 12 ct bins Error for 24 ct bins
Exp + Double gaussian mean Bin content for 8 ct bins Bin content for 12 ct bins Bin content for 24 ct bins
sigma Error for 8 ct bins Error for 12 ct bins Error for 24 ct bins

Clearly with the new model many of the sPlot fits stabilized showing also an increase in the number of toys that successfully enter the cloud distributibutins. However the structures of the multiple peaks are still there and in some cases also more evident than before.

Signal weight distributions for all signal weights

Model Weight distributions Signal mean values for all models Bkg mean values for all models
Cheb1 + Exp + Double gaussian 8 bins 12 bins 24 bins 8 bins 12 bins 24 bins 8 bins 12 bins 24 bins
Cheb0 + Exp + Double gaussian 8 bins 12 bins 24 bins
Cheb1 + Double gaussian 8 bins 12 bins 24 bins
Cheb0 + Double gaussian 8 bins 12 bins 24 bins
Exp + Double gaussian 8 bins 12 bins 24 bins

Weight scatter plot for all components

A main source of the multi-peak structure is the mis-identification of sertain events from background to signal or from one source of background to the other, for this reason the scatter plots between all the weights mass models are plotted for each of the configurations stated above.

Model Scatter plots
Cheb1 + Exp + Double gaussian 8 bins 12 bins 24 bins
Cheb0 + Exp + Double gaussian 8 bins 12 bins 24 bins
Cheb1 + Double gaussian 8 bins 12 bins 24 bins
Cheb0 + Double gaussian 8 bins 12 bins 24 bins

Bin summary plots for toys

With the studies described above the amount of plots produced is becoming very difficult to be handled and studied systematically. For this reason the following set of plots has been decided to be used that will summarize the weight distribution from the toys in each bin for the 8bin configuration along with the error distributions. What is added new is that a custom tagging system is introduced. The custom tagging is applied to the events based on which of the three weights is the highest assinging it as a signal or background event (multiple background components).

Model Distribution type Bin summary plots Weight distribution custom tagging with truth
Cheb1 + Exp + Single Gaus bin content bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7 bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7
Cheb1 + Exp + Double Gaus bin content bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7 bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7
Cheb0 + Exp + Signle Gaus bin content bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7 bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7
Cheb0 + Exp + Double Gaus bin content bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7 bin 0 bin 1 bin 2 bin 3 bin 4 bin 5 bin 6 bin 7

an importan information for decoding the plots shown above is to show what the scatter plot between the number of events in truth and tagged events is. The main motivation is due to the fact that what is attempted to be modelled with the tagging system is the probability to the number of signal tagged events and truth signal tagged events. By looking at the sPlot bin content distribution what we are modeling is the probability to find a certain number of signal tagged events given the probability of having a certain number of truth tagged events. So the only missing component here is the probability of the truth events is: (P(A^B) = P(A|B) * P(B)).

Model Scatter plots for different bins Scatter plots with events for each point
Cheb1 + Exp + Single Gaus Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
Cheb1 + Exp + Two Gaus Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7  
Cheb0 + Exp + Single Gaus Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7  
Cheb0 + Exp + Two Gaus Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7  

Tagging validation

In the plots shown above for the bin content summary canvases a custom method is being to assigna tag (custom) to each of the toy events that is passing the fitting step. The custom tagging method is being employed with the main motivation arising from the multiple peak structure observed in the sPlot signal projection, specially at the lower entries bins. The way the tag is being calculated follows the simple logic of whichever weight assigned in an event has the biggest value tags the event as of this source. For example if the signal weight is larger than the bkg weights then the event is called signal like. To check whether the tagging method is representing the toys correctly the following plots have been produced. On the top left the bin distribution of the given bin is shown as taken from the toys that successfully pass the invariant mass fitting step of sPlot. The stack distribution with the different colors represent the different number of events in a current bin based on the tagging method. For example in bin 5 the black distribution shows all the toy events that contain 0 signal tagged events, red those with 1 signal events and so on.

Model Distribution type Distributions for different bins
Cheb1 + Exp + Single Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
Cheb1 + Exp + Two Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
Cheb0 + Exp + Signle Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7
Cheb0 + Exp + Two Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7

Paremeterization of tagged distributions

The major goal for the tagged distributions is the attempt to paremeterize the distributions of the different number of signal events so that given the entries of the bin the corrected number can be used in the chi2 metric calculation for the histogram content. The first step towards this goal is to calculate the mean and RMS of each of the distributions in every bin and put them in a graph. where the x-axis is the number of events and the y-axis the mean of the histogram along with the RMS shown as the error of th epoints

Model Distribution type Different bins Mean RMS distributions vs number of signal/bkg events RMS Mean vs Number of bkg events for given signal events
Cheb1 + Exp + Single Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7 Mean bin 4 Mean bin 5 Mean bin 6 Mean bin 7 RMS bin 4 RMS bin 5 RMS bin 6 RMS bin 7
Cheb1 + Exp + Two Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7    
Cheb0 + Exp + Single Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7    
Cheb0 + Exp + Two Gaus bin content Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7    

as a cross check additionally the number of background events vs the number of signal events are given in the following plots.

Model Number of signal vs number of bkg events per bin
Cheb1 + Exp + Single Gaus Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Bin 6 Bin 7

Fitting RMS and mean distributions (Bin 5 only)

In this section what is going to be discussed is the attempt to fit the RMS and Mean distribution of the muliple peak in bin 5 as a function the number of background and signal events. The requested fit is a 2D fit and was performed in the following way. Initially the RMS fit is going to be described and shown in this section. For the RMS we expect it grow with the square root of the number of events. Therefore the fitted models has the following parametrization: RMS = C1*Sqrt(NBkg) + C2*Sqrt(NBkg) + C3. The 2D graph created for the data excludes all the points that are 0 when this information is coming from 1 entry histograms. The following plots have been obtained where we see the RMS vs NBkg for a given number of singal events and RMS vs NSig for a given number of background events.

Distribution type Plots
RMS distribution vs signal RMSVsNSig bin 5
RMS distribution vs bkg RMSVsNBkg bin 5

In the final canvas the fit result is being shown where the fit parameters can be summarized in the following table:

Fit parameter Value
SigCoef (C1) 0.07 +/- 0.02
BkgCoef (C2) 0.05 +/- 0.01
ShiftCoef (C3) 0.1 +/- 0.02
Fit log from 2D fit  

RMSMeanParametrization

AnalyticalModelToys

FittingExercise

-- IoannisXiotidis - 2020-07-10

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PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.1_15.0.png r1 manage 15.4 K 2020-07-21 - 16:52 IoannisXiotidis  
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PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.3_15.0.png r1 manage 15.0 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.4_15.0.png r1 manage 15.4 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.0.png r1 manage 18.3 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.1.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.2.png r1 manage 18.6 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.3.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.4.png r1 manage 18.7 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.5.png r1 manage 18.7 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.6.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.7.png r1 manage 18.7 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.8.png r1 manage 18.6 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_10.9.png r1 manage 18.7 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.0.png r1 manage 18.8 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.1.png r1 manage 18.8 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.2.png r1 manage 18.8 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.3.png r1 manage 18.9 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.4.png r1 manage 18.9 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.5.png r1 manage 18.8 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.6.png r1 manage 18.8 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.7.png r1 manage 18.5 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.8.png r1 manage 18.6 K 2020-07-22 - 11:02 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_11.9.png r1 manage 18.5 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.0.png r2 r1 manage 18.7 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.1.png r1 manage 18.7 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.2.png r1 manage 18.9 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.3.png r1 manage 18.9 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.4.png r1 manage 18.7 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.5.png r1 manage 18.7 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.6.png r1 manage 18.8 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.7.png r1 manage 18.8 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.8.png r1 manage 19.0 K 2020-07-22 - 11:03 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_12.9.png r1 manage 18.6 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.0.png r1 manage 18.7 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.1.png r1 manage 18.7 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.2.png r1 manage 18.8 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.3.png r1 manage 18.7 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.4.png r1 manage 18.8 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.5.png r1 manage 19.0 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.6.png r1 manage 19.2 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.7.png r1 manage 19.1 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.8.png r1 manage 19.1 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_13.9.png r1 manage 19.0 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.0.png r1 manage 19.1 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.1.png r1 manage 19.1 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.2.png r1 manage 19.2 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.3.png r1 manage 19.2 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.4.png r1 manage 19.1 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.5.png r1 manage 19.2 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.6.png r1 manage 19.2 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.7.png r1 manage 19.2 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.8.png r1 manage 19.1 K 2020-07-22 - 11:04 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_14.9.png r1 manage 19.0 K 2020-07-22 - 11:05 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_15.0.png r1 manage 13.8 K 2020-07-20 - 11:36 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_15.0_2.png r1 manage 15.5 K 2020-07-21 - 17:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.0.png r1 manage 17.8 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.1.png r1 manage 17.7 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.2.png r1 manage 17.9 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.3.png r1 manage 17.9 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.4.png r1 manage 18.0 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.5.png r1 manage 18.3 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.6.png r1 manage 18.2 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.7.png r1 manage 18.0 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.8.png r1 manage 18.1 K 2020-07-22 - 11:00 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_8.9.png r1 manage 18.0 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.0.png r1 manage 18.1 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.1.png r1 manage 18.4 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.2.png r1 manage 18.3 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.3.png r1 manage 18.3 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.4.png r1 manage 18.3 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.5.png r1 manage 18.2 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.6.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.7.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.8.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.5_9.9.png r1 manage 18.5 K 2020-07-22 - 11:01 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.6_15.0.png r1 manage 15.1 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.7_15.0.png r1 manage 15.1 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.8_15.0.png r1 manage 15.2 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange2.9_15.0.png r1 manage 15.2 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.0_15.0.png r1 manage 14.9 K 2020-07-21 - 16:52 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.1_15.0.png r1 manage 15.2 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.2_15.0.png r1 manage 14.7 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.3_15.0.png r1 manage 15.1 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.4_15.0.png r1 manage 14.9 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.5_15.0.png r1 manage 14.9 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.6_15.0.png r1 manage 14.4 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.7_15.0.png r1 manage 14.9 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.8_15.0.png r1 manage 14.8 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange3.9_15.0.png r1 manage 14.3 K 2020-07-21 - 16:55 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange4.0_15.0.png r1 manage 14.6 K 2020-07-21 - 16:56 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange4.1_15.0.png r1 manage 14.4 K 2020-07-21 - 16:56 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange4.2_15.0.png r1 manage 14.4 K 2020-07-21 - 16:56 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange4.3_15.0.png r1 manage 14.4 K 2020-07-21 - 16:56 IoannisXiotidis  
PNGpng SignalDatasetToy_AcceptReject_GenValue1.73ps_FittingRange4.4_15.0.png r1 manage 14.6 K 2020-07-21 - 16:56 IoannisXiotidis  
PNGpng SignalDatasetToy_SamplingMethod_MassProperTimePlotsForDifferenteLifetime.png r1 manage 23.5 K 2020-07-10 - 12:54 IoannisXiotidis  
PNGpng SignalDatasetToy_SamplingMethod_NumberofEvents_DifferentLifetime.png r1 manage 10.4 K 2020-07-10 - 11:32 IoannisXiotidis  
PNGpng SignalDatasetToy_SamplingMethod_PullsResidualsOfValidationFitOnTruthTime.png r1 manage 15.7 K 2020-07-10 - 12:49 IoannisXiotidis  
PNGpng SignalDatasetToy_SamplingMethod_TimeRequiredPerSampling.png r1 manage 13.0 K 2020-07-10 - 11:43 IoannisXiotidis  
Texttxt ToyFitting_BDT0.3_10ToyFits_FitLog.txt r1 manage 31.8 K 2020-11-24 - 10:27 IoannisXiotidis  
PNGpng TruthSample_ChangingLowerEdge.png r1 manage 11.3 K 2020-09-17 - 16:39 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut-999.0LowerEdge1GenValue1.533_From0.5to15.0.png r1 manage 13.4 K 2020-08-13 - 11:10 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut-999.0LowerEdge1GenValue1.533_From4.0to15.0.png r1 manage 12.5 K 2020-08-13 - 11:10 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAddedNoLxyTriggerNoCutFile_From0.0to15.0.png r1 manage 22.4 K 2020-08-22 - 15:41 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0_15psDoubleFittingAppliedOldErrorStepDefinitionNoRebinningAddedNoLxyTriggerNoCutFile_From0.0to15.0.png r1 manage 22.3 K 2020-09-03 - 10:50 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0_15psNoRebinningAddedLxyTriggerOnly_From0.0to15.0.png r1 manage 19.8 K 2020-09-09 - 12:54 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0_15psNoRebinningAddedNonLxyTriggerOnly_From0.0to15.0.png r1 manage 20.3 K 2020-09-09 - 12:54 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut0.3LowerEdge1GenValue1.533_From0.5to15.0.png r1 manage 13.1 K 2020-08-13 - 11:10 IoannisXiotidis  
PNGpng TruthTimeFits_BDTCut0.3LowerEdge1GenValue1.533_From4.0to15.0.png r1 manage 11.9 K 2020-08-13 - 11:10 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeEvenDataGenValue1.4727NoRebinningAdded.png r1 manage 18.0 K 2020-09-14 - 14:09 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.4727NoRebinningAdded.png r1 manage 19.5 K 2020-09-14 - 14:09 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded.png r1 manage 17.4 K 2020-08-18 - 17:54 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAddedNoLxyTriggerNoCutFile.png r1 manage 19.8 K 2020-08-22 - 15:38 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0_15psDoubleFittingAppliedOldErrorStepDefinitionNoRebinningAddedNoLxyTriggerNoCutFile.png r1 manage 15.9 K 2020-09-03 - 10:50 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0_15psNoRebinningAddedLxyTriggerOnly.png r1 manage 17.1 K 2020-09-09 - 12:49 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.4727SingleFitWithRange0.0_15psNoRebinningAddedNonLxyTriggerOnly.png r1 manage 16.8 K 2020-09-09 - 12:49 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.533DoubleFittingAppliedNewErrorStepDefinitionNoRebinningAdded.png r1 manage 17.4 K 2020-08-18 - 17:16 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.533DoubleFittingAppliedOldErrorStepDefinitionNoRebinningAdded.png r1 manage 17.4 K 2020-08-18 - 17:16 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeGenValue1.533NewErrorStepDefinitionNoRebinningAdded.png r1 manage 17.4 K 2020-08-18 - 16:06 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeLowerEdgeOddDataGenValue1.4727NoRebinningAdded.png r1 manage 19.1 K 2020-09-14 - 14:09 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeEvenDataGenValue1.4727NoRebinningAdded_Lower0.0ps.png r1 manage 18.9 K 2020-09-15 - 14:59 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeEvenDataGenValue1.4727NoRebinningAdded_Lower0.5ps.png r1 manage 18.8 K 2020-09-15 - 14:59 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.4727NoRebinningAdded_Lower0.0ps.png r1 manage 17.4 K 2020-09-15 - 14:59 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.4727NoRebinningAdded_Lower0.5ps.png r1 manage 17.5 K 2020-09-15 - 14:59 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded_0.5psLowerEdge.png r1 manage 17.2 K 2020-08-18 - 17:54 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.4727OldErrorStepDefinitionNoRebinningAdded_6psLowerEdge.png r1 manage 18.6 K 2020-08-18 - 17:54 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.533DoubleFittingAppliedNewErrorStepDefinitionNoRebinningAdded.png r1 manage 19.7 K 2020-08-18 - 17:16 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.533DoubleFittingAppliedOldErrorStepDefinitionNoRebinningAdded.png r1 manage 19.8 K 2020-08-18 - 17:16 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeGenValue1.533NewErrorStepDefinitionNoRebinningAdded.png r1 manage 19.8 K 2020-08-18 - 16:06 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeOddDataGenValue1.4727NoRebinningAdded_Lower0.0ps.png r1 manage 19.1 K 2020-09-15 - 14:59 IoannisXiotidis  
PNGpng TruthTimeFitspValuePlot_BDTCut-999ChangeUpperEdgeOddDataGenValue1.4727NoRebinningAdded_Lower0.5ps.png r1 manage 18.1 K 2020-09-15 - 14:59 IoannisXiotidis  
PNGpng VariableCorrelationWithTruthTime.png r1 manage 73.2 K 2020-09-08 - 15:24 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_BinSummartPlots_Cheb1ExpoOneGaus.png r1 manage 37.7 K 2020-12-17 - 10:34 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_CentralValuesFromsPlots_1000Toys.png r1 manage 131.7 K 2020-11-25 - 22:24 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_CentralValuesFromsPlots_12ctBins_1000Toys.png r1 manage 148.9 K 2020-11-26 - 10:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_CentralValuesFromsPlots_24ctBins_1000Toys.png r1 manage 169.8 K 2020-11-26 - 10:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che0ExpOneGaus_MultiCanvasForBin0o8.png r1 manage 73.6 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che0ExpOneGaus_MultiCanvasForBin1o8.png r1 manage 65.5 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che0ExpOneGaus_MultiCanvasForBin2o8.png r1 manage 62.6 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che0ExpOneGaus_MultiCanvasForBin6o8.png r1 manage 51.9 K 2020-12-29 - 17:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che0ExpOneGaus_MultiCanvasForBin7o8.png r1 manage 53.1 K 2020-12-29 - 17:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin0o8.png r1 manage 61.0 K 2020-12-29 - 17:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin1o8.png r1 manage 74.5 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin2o8.png r1 manage 60.4 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin3o8.png r1 manage 62.7 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin4o8.png r1 manage 57.4 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin6o8.png r1 manage 57.1 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Che1ExpOneGaus_MultiCanvasForBin7o8.png r1 manage 50.8 K 2020-12-29 - 17:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin1o8.png r1 manage 125.2 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin2o8.png r1 manage 123.4 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin3o8.png r1 manage 119.0 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin4o8.png r1 manage 119.7 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin5o8.png r1 manage 107.3 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin6o8.png r1 manage 102.7 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin7o8.png r1 manage 98.9 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Bkg_CutRange_Bin8o8.png r1 manage 102.9 K 2020-11-30 - 19:29 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0ExpTwoGaus_sPlots.png r1 manage 104.1 K 2020-12-14 - 13:37 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Exp_FullRange_CentralValuesFromsPlots_8ctBins_1000Toys.png r1 manage 137.2 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Exp_FullRange_RMSFromsPlots_8ctBins_1000Toys.png r1 manage 144.8 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0ExpoGausModel_FullRange_On10FirstToys.png r1 manage 163.8 K 2020-11-30 - 16:49 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin1o8.png r1 manage 132.0 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin2o8.png r1 manage 132.5 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin3o8.png r1 manage 128.3 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin4o8.png r1 manage 115.1 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin5o8.png r1 manage 119.1 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin6o8.png r1 manage 120.9 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin7o8.png r1 manage 123.1 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0FullBkg_FullRange_Bin8o8.png r1 manage 99.3 K 2020-11-30 - 19:28 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0GausModel_CutRange_On10FirstToys.png r1 manage 134.1 K 2020-11-30 - 16:49 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0Only_CutRange_CentralValuesFromsPlots_8ctBins_1000Toys.png r1 manage 140.1 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb0TwoGaus_sPlots.png r1 manage 83.8 K 2020-12-14 - 13:37 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin1o8.png r1 manage 123.1 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin2o8.png r1 manage 119.7 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin3o8.png r1 manage 121.2 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin4o8.png r1 manage 109.3 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin5o8.png r1 manage 107.5 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin6o8.png r1 manage 101.6 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin7o8.png r1 manage 98.7 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Bkg_CutRange_Bin8o8.png r1 manage 100.4 K 2020-11-30 - 19:31 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1ExpTwoGaus_sPlots.png r1 manage 103.4 K 2020-12-14 - 13:37 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1GausModel_CutRange_On10FirstToys.png r1 manage 140.3 K 2020-11-30 - 16:49 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Only_CutRange_CentralValuesFromsPlots_8ctBins_1000Toys.png r1 manage 134.6 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1Only_CutRange_RMSFromsPlots_8ctBins_1000Toys.png r1 manage 147.3 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_Cheb1TwoGaus_sPlots.png r1 manage 86.3 K 2020-12-14 - 13:37 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0ExpOneGaus_12bins.png r1 manage 69.3 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0ExpOneGaus_24bins.png r1 manage 80.7 K 2020-12-11 - 16:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0ExpTwoGaus_12bins.png r1 manage 70.2 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0ExpTwoGaus_24bins.png r1 manage 77.2 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0ExpTwoGaus_8bins.png r1 manage 64.0 K 2020-12-14 - 13:36 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0OneGaus_12bins.png r1 manage 65.6 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0OneGaus_24bins.png r1 manage 77.5 K 2020-12-11 - 16:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0TwoGaus_12bins.png r1 manage 69.5 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0TwoGaus_24bins.png r1 manage 77.9 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb0TwoGaus_8bins.png r1 manage 65.6 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1ExpTwoGaus_12bins.png r1 manage 69.5 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1ExpTwoGaus_24bins.png r1 manage 79.6 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1ExpTwoGaus_8bins.png r1 manage 60.5 K 2020-12-14 - 13:37 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1OneGaus_12bins.png r1 manage 64.8 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1OneGaus_24bins.png r1 manage 76.1 K 2020-12-11 - 16:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1TwoGaus_12bins.png r1 manage 68.4 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1TwoGaus_24bins.png r1 manage 81.0 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_Cheb1TwoGaus_8bins.png r1 manage 64.7 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_ExpTwoGaus_12bins.png r1 manage 66.5 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_ExpTwoGaus_24bins.png r1 manage 77.9 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_ExpTwoGaus_8bins.png r1 manage 63.3 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_ExpoOneGaus_12bins.png r1 manage 59.3 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ContentDistributions_ExpoOneGaus_24bins.png r1 manage 74.4 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb0ExpTwoGaus_12bins.png r1 manage 72.6 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb0ExpTwoGaus_24bins.png r1 manage 81.5 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb0ExpTwoGaus_8bins.png r1 manage 66.8 K 2020-12-14 - 13:36 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb0TwoGaus_12bins.png r1 manage 73.0 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb0TwoGaus_24bins.png r1 manage 80.6 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb0TwoGaus_8bins.png r1 manage 63.6 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb1ExpTwoGaus_12bins.png r1 manage 73.3 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb1ExpTwoGaus_24bins.png r1 manage 83.5 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb1ExpTwoGaus_8bins.png r1 manage 63.8 K 2020-12-14 - 13:36 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb1TwoGaus_12bins.png r1 manage 72.2 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb1TwoGaus_24bins.png r1 manage 83.8 K 2020-12-14 - 13:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_Cheb1TwoGaus_8bins.png r1 manage 68.5 K 2020-12-14 - 13:36 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_ExpTwoGaus_12bins.png r1 manage 71.0 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_ExpTwoGaus_24bins.png r1 manage 81.5 K 2020-12-14 - 13:39 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistribution_ExpTwoGaus_8bins.png r1 manage 63.7 K 2020-12-14 - 13:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_Cheb0ExpOneGaus_12bins.png r1 manage 70.4 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_Cheb0ExpOneGaus_24bins.png r1 manage 83.4 K 2020-12-11 - 16:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_Cheb0OneGaus_12bins.png r1 manage 64.4 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_Cheb0OneGaus_24bins.png r1 manage 76.1 K 2020-12-11 - 16:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_Cheb1OneGaus_12bins.png r1 manage 64.9 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_Cheb1OneGaus_24bins.png r1 manage 74.1 K 2020-12-11 - 16:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_ExpoOneGaus_12bins.png r1 manage 55.2 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ErrorDistributions_ExpoOneGaus_24bins.png r1 manage 71.2 K 2020-12-11 - 16:41 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ExpTwoGaus_sPlots.png r1 manage 81.6 K 2020-12-14 - 14:12 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ExpoGausModel_CutRange_On10FirstToys.png r1 manage 137.8 K 2020-11-30 - 16:49 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ExpoOnly_CutRange_CentralValuesFromsPlots_8ctBins_1000Toys.png r1 manage 133.5 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_ExpoOnly_CutRange_RMSFromsPlots_8ctBins_1000Toys.png r1 manage 141.1 K 2020-11-30 - 17:11 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin1o8.png r1 manage 118.0 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin2o8.png r1 manage 121.4 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin3o8.png r1 manage 116.8 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin4o8.png r1 manage 125.3 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin5o8.png r1 manage 117.8 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin6o8.png r1 manage 112.4 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin7o8.png r1 manage 112.4 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_FullBkg_FullRange_Bin8o8.png r1 manage 98.6 K 2020-11-30 - 19:27 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_MeanValueOfBkgEventsInToys_AllModels_12Bins.png r1 manage 30.0 K 2020-12-14 - 16:53 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_MeanValueOfBkgEventsInToys_AllModels_24Bins.png r1 manage 32.1 K 2020-12-14 - 17:02 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_MeanValueOfBkgEventsInToys_AllModels_8Bins.png r1 manage 29.0 K 2020-12-14 - 16:53 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_MeanValueOfSignalEventsInToys_AllModels_12Bins.png r1 manage 32.9 K 2020-12-14 - 16:53 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_MeanValueOfSignalEventsInToys_AllModels_24Bins.png r1 manage 32.9 K 2020-12-14 - 17:02 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_MeanValueOfSignalEventsInToys_AllModels_8Bins.png r1 manage 33.3 K 2020-12-14 - 16:53 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_On10FirstToys.png r1 manage 164.8 K 2020-11-25 - 22:14 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_RMSFromsPlots_1000Toys.png r2 r1 manage 141.6 K 2020-11-26 - 10:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_RMSFromsPlots_12ctBins_1000Toys.png r1 manage 158.1 K 2020-11-26 - 10:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_RMSFromsPlots_24ctBins_1000Toys.png r1 manage 179.6 K 2020-11-26 - 10:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb0ExpoTwoGaus_12bins.png r1 manage 54.4 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb0ExpoTwoGaus_24bins.png r1 manage 62.9 K 2020-12-14 - 14:43 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb0ExpoTwoGaus_8bins.png r1 manage 44.0 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb0TwoGaus_12bins.png r1 manage 55.3 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb0TwoGaus_24bins.png r1 manage 60.8 K 2020-12-14 - 14:43 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb0TwoGaus_8bins.png r1 manage 39.8 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb1ExpoTwoGaus_12bins.png r1 manage 54.3 K 2020-12-14 - 14:43 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb1ExpoTwoGaus_24bins.png r1 manage 63.1 K 2020-12-14 - 14:43 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb1ExpoTwoGaus_8bins.png r1 manage 41.9 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb1TwoGaus_12bins.png r1 manage 53.1 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb1TwoGaus_24bins.png r1 manage 61.5 K 2020-12-14 - 14:43 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightDist_Cheb1TwoGaus_8bins.png r1 manage 39.5 K 2020-12-14 - 14:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatterPlotsForAllModels_bkgEvents.png r1 manage 64.3 K 2020-12-03 - 10:21 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb0ExpoTwoGaus_12bins.png r1 manage 84.7 K 2020-12-14 - 15:48 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb0ExpoTwoGaus_24bins.png r1 manage 90.6 K 2020-12-14 - 15:48 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb0ExpoTwoGaus_8bins.png r1 manage 70.5 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb0TwoGaus_12bins.png r1 manage 57.0 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb0TwoGaus_24bins.png r1 manage 51.8 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb0TwoGaus_8bins.png r1 manage 43.9 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb1ExpoTwoGaus_12bins.png r1 manage 73.3 K 2020-12-14 - 15:48 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb1ExpoTwoGaus_24bins.png r1 manage 79.6 K 2020-12-14 - 15:48 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb1ExpoTwoGaus_8bins.png r1 manage 58.1 K 2020-12-14 - 15:48 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb1TwoGaus_12bins.png r1 manage 54.7 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb1TwoGaus_24bins.png r1 manage 62.3 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_WeightScatter_Cheb1TwoGaus_8bins.png r1 manage 43.1 K 2020-12-14 - 15:47 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin0o8.png r1 manage 63.1 K 2021-01-05 - 08:21 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin1o8.png r1 manage 69.6 K 2021-01-05 - 08:21 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin2o8.png r1 manage 58.3 K 2021-01-05 - 08:21 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin3o8.png r1 manage 50.0 K 2021-01-05 - 08:21 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin4o8.png r1 manage 46.7 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin5o8.png r1 manage 44.5 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin6o8.png r1 manage 41.2 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin7o8.png r1 manage 43.7 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin0o8.png r1 manage 69.7 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin1o8.png r1 manage 72.1 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin2o8.png r1 manage 73.0 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin3o8.png r1 manage 60.9 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin4o8.png r1 manage 56.1 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin5o8.png r1 manage 49.5 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin6o8.png r1 manage 46.6 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_MultiCanvasForBin7o8.png r1 manage 48.2 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_RMSMultiPeakVsBkgWithFitProj.png r1 manage 36.8 K 2021-01-21 - 11:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_RMSMultiPeakVsSignalWithFitProj.png r1 manage 43.8 K 2021-01-21 - 11:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct0o8.png r1 manage 52.2 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct1o8.png r1 manage 58.6 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct2o8.png r1 manage 51.1 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct3o8.png r1 manage 41.7 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct4o8.png r1 manage 36.0 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct5o8.png r1 manage 32.4 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct6o8.png r1 manage 36.3 K 2021-01-05 - 17:08 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct7o8.png r1 manage 35.8 K 2021-01-05 - 17:08 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas0o8.png r1 manage 66.3 K 2021-01-04 - 22:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas1o8.png r1 manage 63.3 K 2021-01-04 - 22:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas2o8.png r1 manage 56.4 K 2021-01-04 - 22:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas3o8.png r1 manage 53.1 K 2021-01-04 - 22:38 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas4o8.png r1 manage 48.2 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas5o8.png r1 manage 44.4 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas6o8.png r1 manage 40.9 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_StackCanvas7o8.png r1 manage 43.7 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_0o8.png r1 manage 33.7 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_1o8.png r1 manage 34.4 K 2021-01-04 - 11:18 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_2o8.png r1 manage 36.1 K 2021-01-04 - 11:19 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_3o8.png r1 manage 34.6 K 2021-01-04 - 11:19 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_4o8.png r1 manage 31.7 K 2021-01-04 - 11:19 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_5o8.png r1 manage 28.5 K 2021-01-04 - 11:19 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_6o8.png r1 manage 29.9 K 2021-01-04 - 11:19 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel0_TaggingEventsWithTruthTag_7o8.png r1 manage 31.5 K 2021-01-04 - 11:19 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin0o8.png r1 manage 57.0 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin1o8.png r1 manage 59.4 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin2o8.png r1 manage 54.0 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin3o8.png r1 manage 48.9 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin4o8.png r1 manage 47.5 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin5o8.png r1 manage 43.9 K 2021-01-05 - 08:22 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin6o8.png r1 manage 43.5 K 2021-01-05 - 08:23 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MeanRMSForDiffSignalNumberOfEventsForBin7o8.png r1 manage 41.0 K 2021-01-05 - 08:23 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin0o8.png r1 manage 75.0 K 2021-01-04 - 11:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin1o8.png r1 manage 80.5 K 2021-01-04 - 11:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin2o8.png r1 manage 72.9 K 2021-01-04 - 11:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin3o8.png r1 manage 62.7 K 2021-01-04 - 11:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin4o8.png r1 manage 54.4 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin5o8.png r1 manage 49.9 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin6o8.png r1 manage 45.8 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_MultiCanvasForBin7o8.png r1 manage 48.4 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct0o8.png r1 manage 51.8 K 2021-01-05 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct1o8.png r1 manage 59.2 K 2021-01-05 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct2o8.png r1 manage 50.0 K 2021-01-05 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct3o8.png r1 manage 38.1 K 2021-01-05 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct4o8.png r1 manage 34.1 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct5o8.png r1 manage 32.3 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct6o8.png r1 manage 36.1 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_ScatterNEventsSignalTruthVsTagged_Forct7o8.png r1 manage 36.1 K 2021-01-05 - 17:05 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas0o8.png r1 manage 59.6 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas1o8.png r1 manage 64.6 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas2o8.png r1 manage 54.1 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas3o8.png r1 manage 53.5 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas4o8.png r1 manage 45.5 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas5o8.png r1 manage 45.1 K 2021-01-04 - 22:40 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas6o8.png r1 manage 40.1 K 2021-01-04 - 22:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_StackCanvas7o8.png r1 manage 44.3 K 2021-01-04 - 22:42 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_0o8.png r1 manage 35.0 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_1o8.png r1 manage 35.9 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_2o8.png r1 manage 32.8 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_3o8.png r1 manage 32.8 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_4o8.png r1 manage 33.1 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_5o8.png r1 manage 29.7 K 2021-01-04 - 11:25 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_6o8.png r1 manage 29.4 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb0ExpoGausPDF_NewModel1_TaggingEventsWithTruthTag_7o8.png r1 manage 32.2 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin0o8.png r1 manage 67.6 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin1o8.png r1 manage 69.9 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin2o8.png r1 manage 66.1 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin3o8.png r1 manage 59.5 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin4o8.png r1 manage 54.7 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin5o8.png r1 manage 48.4 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin6o8.png r1 manage 49.4 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_MultiCanvasForBin7o8.png r1 manage 45.7 K 2021-01-04 - 11:35 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin0o8.png r1 manage 57.5 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin1o8.png r1 manage 63.6 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin2o8.png r1 manage 54.2 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin3o8.png r1 manage 48.2 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin4o8.png r1 manage 46.7 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin5o8.png r1 manage 43.8 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin6o8.png r1 manage 43.2 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_MeanRMSForDiffSignalNumberOfEventsForBin7o8.png r1 manage 43.2 K 2021-01-05 - 08:20 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct0o8.png r1 manage 60.1 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct1o8.png r1 manage 68.1 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct2o8.png r1 manage 56.8 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct3o8.png r1 manage 50.1 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct4o8.png r1 manage 46.3 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct5o8.png r1 manage 43.5 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct6o8.png r1 manage 43.5 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterMeanAndARMSWithNSigAndNBkg_forct7o8.png r1 manage 44.0 K 2021-01-12 - 09:30 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct0o8.png r1 manage 58.0 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct1o8.png r1 manage 67.7 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct2o8.png r1 manage 63.5 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct3o8.png r1 manage 48.4 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct4o8.png r1 manage 42.5 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct5o8.png r1 manage 35.1 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct6o8.png r1 manage 36.7 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTaggedHist_Forct7o8.png r1 manage 36.7 K 2021-01-10 - 17:04 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct0o8.png r1 manage 52.6 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct1o8.png r1 manage 57.4 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct2o8.png r1 manage 50.0 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct3o8.png r1 manage 39.9 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct4o8.png r1 manage 35.4 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct5o8.png r1 manage 32.1 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct6o8.png r1 manage 36.0 K 2021-01-05 - 17:01 IoannisXiotidis  
PNGpng sPlotOnToys_BDT0.365_cheb1ExpoGausPDF_NewModel0_ScatterNEventsSignalTruthVsTagged_Forct7o8.png r1 manage 35.8 K 2021-01-05 - 17:01 IoannisXiotidis  
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