Kyle Cranmer (NYU) OKC Prospects, Stockholm, Sept. 16, 2010
Cosmology and Particle Physics
Kyle Cranmer,
New York University
The Theory/Experiment Interface:
Publishing the Likelihood Function with the RooFit/RooStats Workspace
1
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Some Personal History
2
Kyle Cranmer (NYU)
Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Some Personal History
2
Archbishop of Canterbury Thomas Cranmer (born: 1489, executed:
1556) author of the “Book of
Common Prayer”
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Some Personal History
2
Archbishop of Canterbury Thomas Cranmer (born: 1489, executed:
1556) author of the “Book of Common Prayer”
Two centuries later (when this Book had become an official prayer book of the Church of England) Thomas Bayes was a non-conformist minister
(Presbyterian) who refused to use
Cranmerʼs book
Kyle Cranmer (NYU)
Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010 3
“Bayesians address the question everyone is interested in, by using assumptions no-one believes”
“Frequentists use impeccable logic to deal with an issue of no interest to anyone”
-L. Lyons
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
A few points
Objective part of Bayesian inference is encoded in Likelihood
‣ improvements in Likelihood is not Bayesian vs. Frequentist
Prior may be based on data
‣ but it also depends on the initial prior
In the same way that the “Bayesian calculus” allows for
propagation of belief, the measurements can be combined with the likelihood function
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P (theory |data) = L(data |theory)π(theory) P (data)
π(theory) ∝ L � (data’ |theory)η(theory) η(theory)
L tot (data’ |theory) = L(data’|theory)L � (data’ |theory)
Kyle Cranmer (NYU)
Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
LHC Data Likelihood
Functions
Interpretation
Fundamental Lagrangian
Data Modeling Data Modeling
CMB
Dark Matter Searche
Data Modeling
Ideal scenario
The ideal scenario for the interface between the data and the inference to the fundamental lagrangian parameters is through a likelihood
function that accurately incorporates all the experimental systematics and retains as much power in the data as possible
Is this feasible?
‣ It is the basic model on which Zfitter, GFitter, SFitter, Fittino, MasterCode, Kismet, SuperBayes, etc. are based
‣ unfortunately, likelihood functions are usually simplistic and based on a few 1-d measurements
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Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Current scenario
Taken from the GFitter paper
6
Eur. Phys. J. C (2009) 60: 543–583 557
information available for 10 discrete data points in the mass range 155 ≤ M H ≤ 200 GeV based on prelimi- nary searches using data samples of up to 3 fb −1 inte- grated luminosity [73]. For the mass range 110 ≤ M H ≤ 200 GeV, Tevatron results based on 2.4 fb −1 are provided for −2 ln Q [ 72], however not for the corresponding con- fidence levels.
To include the direct Higgs searches in the complete SM fit we interpret the −2 ln Q results for a given Higgs mass hypothesis 23 as measurements and derive a log- likelihood estimator quantifying the deviation of the data from the corresponding SM Higgs expectation. For this purpose we transform the one-sided CL s +b into two- sided confidence levels 24 using CL 2 - sided
s +b = 2CL s +b for CL s +b ≤ 0.5 and CL 2 - sided
s +b = 2(1 − CL s +b ) for CL s +b >
0.5. The contribution to the χ 2 estimator of the fit is then obtained via δχ 2 = 2·[Erf −1 (1 −CL 2 - sided
s +b ) ] 2 , where Erf −1 is the inverse error function, 25 and where the under- lying probability density function has been assumed to be symmetric (cf. footnote 21 on p. 556).
For the complete mass range available for the LEP searches (M H ≤ 120 GeV), and for the high-mass region of the Tevatron searches (155 ≤ M H ≤ 200 GeV), we em- ploy the CL s +b values determined by the experiments. For the low-mass Tevatron results (110 ≤ M H ≤ 150 GeV), where the CL s +b values are not provided, they are esti- mated from the measured −2 ln Q values that are com-
23 This procedure only uses the M H value under consideration, where Higgs-mass hypothesis and measurement are compared. It thus ne- glects that in the SM a given signal hypothesis entails background hy- potheses for all M H values other than the one considered. An analysis accounting for this should provide a statistical comparison of a given hypothesis with all available measurements. This however would re- quire to know the correlations among all the measurement points (or better: the full experimental likelihood as a function of the Higgs-mass hypothesis), which are not provided by the experiments to date. The difference to the hypothesis-only test employed here is expected to be small at present, but may become important once an experimental Higgs signal appears, which however has insufficient significance yet to claim a discovery (which would allow one to discard all other Higgs- mass hypotheses). We thank Bill Murray (RAL) for bringing this point to our attention.
24 The experiments integrate only the tail towards larger −2 ln Q values of the probability density function to compute CL s +b (corresponding to a counting experiment with to too few observed events with respect to the s + b hypothesis), which is later used to derive CL s in the modified frequentest approach. They thus quantify Higgs-like (not necessarily SM Higgs) enhancements in the data. In the global SM fit, however, one is interested in the compatibility between the SM hypothesis and the experimental data as a whole, and must hence account for any devi- ation, including the tail towards smaller −2 ln Q values (corresponding to a counting experiment with too many Higgs candidates with respect to the s + b hypothesis where, s labels the SM Higgs signal).
25 The use of Erf −1 provides a consistent error interpretation when (re)translating the χ 2 estimator into a confidence level via CL = 1 − Prob(χ 2 , 1) = Erf( !
χ 2 /2).
pared with those expected for the s +b hypothesis, and us- ing the errors derived by the experiments for the b hypoth- esis. We have tested this approximation in the high-mass region, where the experimental values of CL s +b from the Tevatron are provided, and found a systematic overesti- mation of the contribution to our χ 2 test statistics of about 30%, with small dependence on the Higgs mass. We thus rescale the test statistics in the mass region where the CL s +b approximation is used (i.e. 110 ≤ M H ≤ 150 GeV) by the correction factor 0.77. 26 Once made available by the TEVNPH Working Group, this approximation will be replaced by the published CL s +b values.
Our method follows the spirit of a global SM fit and takes advantage from downward fluctuations of the back- ground in the sensitive region to obtain a more restrictive limit on the SM Higgs production as is obtained with the modified frequentest approach. The resulting χ 2 curves versus M H are shown in Fig. 4.1. The low-mass exclu- sion is dominated by the LEP searches, while the infor- mation above 120 GeV is contributed by the Tevatron experiments. Following the original figure, the Tevatron measurements have been interpolated by straight lines for the purpose of presentation and in the fit which deals with continuous M H values.
Constraints on the weak mixing angle can also be derived from atomic parity violation measurements in caesium, thal- lium, lead and bismuth. For heavy atoms one determines the weak charge, Q W ≈ Z(1 − 4 sin 2 θ W ) − N. Because the present experimental accuracy of 0.6% (3.2%) for Q W from Cs [77, 78] (Tl [79, 80]) is still an order of magnitude away from a competitive constraint on sin 2 θ W , we do not include it into the fit. (Including it would reduce the error on the fitted Higgs mass by 0.2 GeV.) Due to the same reason we do not include the parity violation left-right asymmetry measurement using fixed target polarised Møller scattering at low Q 2 = 0.026 GeV 2 [81]. 27
The NuTeV Collaboration measured ratios of neutral and charged current cross sections in neutrino–nucleon scatter- ing at an average Q 2 $ 20 GeV 2 using both muon neutrino and muon anti-neutrino beams [82]. The results derived for the effective weak couplings are not included in this analy- sis because of unclear theoretical uncertainties from QCD effects such as next-to-leading order corrections and nu- clear effects of the bound nucleon parton distribution func- tions [83] (for reviews see, e.g., Refs. [84, 85]).
Although a large number of precision results for α S at various scales are available, including recent 3NLO deter- minations at the τ -mass scale [17, 18, 86, 87], we do not
26 The correction factor reduces the value of the χ 2 test statistics. As described in footnote 32, its application has little impact on the fit re- sults.
27 The main success of this measurement is to have established the run-
ning of the weak coupling strength at the 6.4σ level.
Kyle Cranmer (NYU)
Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
The situation 10 years ago...
7
Origins I: The First “Statistics in HEP” conference
But a practical problem remained: How to communicate multi-D likelihood?
!"#$%&'()*%+',)-.+'./'0%')01%'./'2)1231).%'2/4%$)5%6'7/-'8$//*',)-.+'./')01%'./'2)1231).%'5//&-%++'/9'9:.;!
http://indico.cern.ch/conferenceDisplay.py?confId=100458
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Outline
Information:
‣ What is the RooStats Project?
‣ What the workspace can do for SUSY/BSM Fits
‣ Real-life examples from the LHC
Example Use cases
‣ A critical look at the weak points in our current chain
Moving forward:
‣ Hard problems that can be solved with planning
‣ Making a clear request to the experiments (discussion)
‣ Preparing toy benchmark examples (discussion)
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Kyle Cranmer (NYU)
Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
RooStats: Project info
Started in 2005, when René Brun asked me to help organize statistical tools in ROOT
‣ Main goals are to provide a common
framework for various statistical techniques (Frequentist, Bayesian, Likelihood based,...)
We want tools to work with probability models of arbitrary complexity (which implies interfaces, etc.)
‣ Decided to base tools on RooFit’s data modeling language and core interfaces
Initially an ATLAS/CMS project, but other experiments are interested (LHCb, Fermi, ...)
‣ core developers
● K. Cranmer (ATLAS), Lorenzo Moneta (ROOT), Gregory Schott (CMS), Wouter Verkerke (RooFit)
‣ open project, you are welcome to contribute
● ~10 others contributing now, growing fast
Included since ROOT v5.22 (we are now on 5.27)
9 https://twiki.cern.ch/twiki/bin/view/RooStats/WebHome
RooStats has been a topic of
conversation in every combined ATLAS/
CMS statistics forum meeting
‣ In July, we showed the first toy
ATLAS/CMS Higgs combination using the tools of RooFit/RooStats.
‣ see agenda:
http://indico.cern.ch/conferenceDisplay.py?confId=100458
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Major Goals and Status
Goal: Standardize interface for major statistical procedures so that they can work on an arbitrary RooFit model & dataset and handle many parameters of interest and nuisance parameters (systematics).
‣ Status: Done
● ConfIntervalCalculator & HypoTestCalculator interface for tools
● they return ConfidenceInterval and HypoTestResult
Goal: Implement most accepted techniques from Frequentist, Bayesian, and Likelihood-based approaches
‣ Status: Done / Ongoing
● ProfileLikelihoodCalculator: (Likelihood) the method of MINUIT/MINOS
● FeldmanCousins: (Frequentist) a generalization of F-C that can incorporate systematics
● MCMCCalculator: (Bayesian) uses Metropolis-Hastings algorithm (native or BAT)
● HybridCalculator: (Bayesian/Frequentist Hybrid) used at LEP and Tevatron
Goal: Provide utilities to perform combined measurements
‣ Status: Partially done / Ongoing
● RooWorkspace allows one to save arbitrary RooFit model (even with custom code) into a .root file. PDFs and DataSets have been extended to facilitate combinations.
● Same technology can aid in digital publishing
10
today’s focus
Kyle Cranmer (NYU)
Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
What goes in a Workspace
The workspace stores the full probability model and any data necessary to evaluate the likelihood function
‣ it is the code necessary to evaluate the likelihood function at an arbitrary point in the parameter space. It is not a big table of likelihood values!
‣ we are using the same ROOT technology that the LHC experiments are using to save their data
● well supported, and supports “schema evolution” / backwards compatibility
‣ the probability model also allows you to generate toy data for any given parameter point
● necessary for frequentist methods, goodness of fit, coverage
‣ PDFs and functions can be extended by the user (source stored in workspace) I will show some visualization of real-life LHC probability models. Let’s start with a simple example:
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G(x |µ, σ)
RooRealVar : µ
RooRealVar : x RooRealVar : σ
RooGaussian : G
Figure 2: test
2 Parameter Estimation
3 Test Statistics and Sampling Distributions
3.1 TestStatistic interface and implementations
We added a new interface class called TestStatistic. It defines the method Evaluate(data, parameterPoint), which returns a double. This class can be used in conjunction with the ToyMCSampler class to generate sampling distributions for a user-defined test statistic.
The following concrete implementations of the TestStatistic interface are currently available ProfileLikelihoodTestStatReturns the log of profile likelihood ratio. Generally a powerful test statistic. NumEventsTestStatReturns the number of events in the dataset. Useful for number counting experiments. DebuggingTestStat Simply returns a uniform random number between 0,1. Useful for debugging. SamplingDistribution and the TestStatSampler interface and implementations
We introduced a “result” or data model class called SamplingDistribution, which holds the sampling distribution of an arbitrary real valued test statistic. The class also can return the inverse of the cumulative distribution function (with or without interpolation).
We introduced an interface for any tool that can produce a SamplingDistribution, called TestStatSampler. The interface is essentially GetSamplingDistribution(parameterPoint) which returns a SamplingDistribution based on a given probability density function. We foresee a few versions of this tool based on toy Monte Carlo, importance sampling, Fourier transforms, etc.
The following concrete implementation of the TestStatSampler interface are currently available ToyMCSamplerUses a Toy Monte Carlo approach to build the sampling distribution. The pdf’s generate method to generate is used to generate toy data, and then the test statistic is evaluated at the requested parameter point. DebuggingSampler Simply returns a uniform distribution between 0,1. Useful for debugging. NeymanConstruction and FeldmanCousins
A flexible framework for the Neyman Construction was added in this release. The Ney- manConstruction is a concrete implementation of the IntervalCalculator interface, but it needs several additional components to be specified before use. The design factorizes the choice
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Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
RooFit: A data modeling toolkit
12
Wouter Verkerke, UCSB
Building realistic models
– Composition (‘plug & play’)
– Convolution
g(x;m,s) m(y;a 0 ,a 1 )
=
! =
g(x,y;a0,a1,s)
Possible in any PDF
No explicit support in PDF code needed
Wouter Verkerke, UCSB
Building realistic models
• Complex PDFs be can be trivially composed using operator classes – Addition
– Multiplication
+ =
* =
Wouter Verkerke, UCSB
Parameters of composite PDF objects
RooAddPdf
sum
RooGaussian
gauss1
RooGaussian
gauss2
RooArgusBG
argus
RooRealVar
g1frac
RooRealVar
g2frac
RooRealVar
x
RooRealVar
sigma
RooRealVar
mean1
RooRealVar
mean2
RooRealVar
argpar
RooRealVar
cutoff
RooArgSet *paramList = sum.getParameters(data) ; paramList->Print("v") ;
RooArgSet::parameters:
1) RooRealVar::argpar : -1.00000 C 2) RooRealVar::cutoff : 9.0000 C 3) RooRealVar::g1frac : 0.50000 C 4) RooRealVar::g2frac : 0.10000 C 5) RooRealVar::mean1 : 2.0000 C 6) RooRealVar::mean2 : 3.0000 C 7) RooRealVar::sigma : 1.0000 C
The parameters of sum are the combined parameters
of its components
A major tool at BaBar. Fit complicated models with >100 parameters!
Kyle Cranmer (NYU)
Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010 13
The RooFit/RooStats workspace
RooStat’s Workspace can save in a file the full likelihood model and the minimal data necessary to reproduce likelihood function.
The technology is generic, we decide how to parametrize the model.
Being used by ATLAS/CMS for very complicated models
Need this for combinations, exciting potential
for publishing results.
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Extracting Contours from these results
• One can plot 2-d contours, 1-d likelihood functions.
• One can evaluate likelihood in N-d and use to evaluate a theoretical model
• If the model has nuisance parameters for systematics, they will be included!
• Easy to combine multiple measurements
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• The workspace can represent arbitrary models with many parameters of interest and many nuisance parameters
Taken from Wouter Verkerke, NIKHEF
This contour is NOT an ellipse!
Kyle Cranmer (NYU)
Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
Examples of Real-Life LHC Models
15
Kyle Cranmer (NYU)
Center for Cosmology and Particle Physics
OKC Prospects, Stockholm, Sept. 16, 2010
ATLAS H->γγ
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RooSimultaneoussimPdf_reparametrized_hgg RooSuperCategoryfitCatRooCategoryetaCat
RooCategoryconvCat
RooCategoryjetCat
RooAddPdfsumPdf_{etaGood;noConv;noJets}_reparametrized_hgg RooProdPdfBackground_{etaGood;noConv;noJets} Hfitter::MggBkgPdfBackground_mgg_{etaGood;noConv;noJets}
RooRealVaroffset
RooRealVarxi_noJets
RooRealVarmgg RooAddPdfBackground_cosThStar_{etaGood;noConv;noJets}
RooGaussianBackground_bump_cosThStar_2RooRealVarbkg_csth02
RooRealVarbkg_csthSigma2 RooRealVarcosThStar
RooRealVarbkg_csthRelNorm2 RooGaussianBackground_bump_cosThStar_1_{etaGood;noConv;noJets}
RooRealVarbkg_csth01_{etaGood;noJets}
RooRealVarbkg_csthSigma1_{etaGood;noJets} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaGood;noConv;noJets}
RooRealVarbkg_csthCoef3
RooRealVarbkg_csthCoef5 RooRealVarbkg_csthPower_{etaGood;noJets}
RooRealVarbkg_csthCoef1_{etaGood;noJets}
RooRealVarbkg_csthCoef2_{etaGood;noJets}
RooRealVarbkg_csthCoef4_{etaGood;noJets}
RooRealVarbkg_csthCoef6_{etaGood;noJets}
RooRealVarbkg_csthRelNorm1_{etaGood;noJets} RooAddPdfBackground_pT_{etaGood;noConv;noJets}
Hfitter::HftPolyExpBackground_pT_3
RooRealVarbkg_ptM0
RooRealVarbkg_ptPow3
RooRealVarbkg_ptExp3 RooRealVarpT RooRealVarbkg_ptTail2Norm
RooGaussianBackground_pT_TC
RooRealVarbkg_ptTCS RooRealVarbkg_ptTailTCNorm Hfitter::HftPolyExpBackground_pT_2_{etaGood;noConv;noJets}
RooRealVarbkg_ptPow2_noJets
RooRealVarbkg_ptExp2_noJets Hfitter::HftPolyExpBackground_pT_1_{etaGood;noConv;noJets}
RooRealVarbkg_ptPow1_noJets
RooRealVarbkg_ptExp1_noJets RooRealVarbkg_ptTail1Norm_noJets RooProdPdfSignal_{etaGood;noConv;noJets} RooAddPdfSignal_mgg_{etaGood;noConv;noJets}
RooGaussianSignal_mgg_T
ail
RooRealVarmTail
RooRealVarsigTail RooCBShapeSignal_mgg_Peak_{etaGood;noConv;noJets}
RooFormulaVarmHiggsFormula_{etaGood;noConv;noJets}
RooRealVarmHiggs RooRealVardmHiggs_{etaGood;noConv} RooRealVarmRes_{etaGood;noConv}
RooRealVartailAlpha_{etaGood;noConv}
RooRealVartailN_{etaGood;noConv}
RooRealVarmggRelNorm_{etaGood;noConv} RooAddPdfSignal_cosThStar_{etaGood;noConv;noJets} RooGaussianSignalcsthstr_1_{etaGood;noConv;noJets}
RooRealVarsig_csthstrSigma_1RooRealVarsig_csthstr0_1_{etaGood;noJets} RooGaussianSignalcsthstr_2_{etaGood;noConv;noJets}
RooRealVarsig_csthstrSigma_2RooRealVarsig_csthstr0_2_{etaGood;noJets} RooRealVarsig_csthstrRelNorm_1_{etaGood;noJets}
RooAddPdfSignal_pT_{etaGood;noConv;noJets}
RooBifurGaussSignalpT_4
RooRealVarsig_pt0_4
RooRealVarsig_ptSigmaL_4
RooRealVarsig_ptSigmaR_4 RooRealVarsig_ptRelNorm_4 RooBifurGaussSignalpT_1_{etaGood;noConv;noJets}
RooRealVarsig_pt0_1_noJets
RooRealVarsig_ptSigmaL_1_noJets
RooRealVarsig_ptSigmaR_1_noJets RooGaussianSignalpT_2_{etaGood;noConv;noJets}
RooRealVarsig_pt0_2_noJets
RooRealVarsig_ptSigma_2_noJets RooGaussianSignalpT_3_{etaGood;noConv;noJets}
RooRealVarsig_pt0_3_noJets
RooRealVarsig_ptSigma_3_noJets RooRealVarsig_ptRelNorm_1_noJets
RooRealVarsig_ptRelNorm_2_noJets RooRealVarn_Background_{etaGood;noConv;noJets}
RooFormulaVarnCat_Signal_{etaGood;noConv;noJets}_reparametrized_hggRooRealVarf_Signal_{etaGood;noConv;noJets}
RooProductnew_n_Signal
RooRealVarmu
RooRealVarn_Signal
RooAddPdfsumPdf_{etaMed;noConv;noJets}_reparametrized_hgg
RooProdPdfBackground_{etaMed;noConv;noJets} Hfitter::MggBkgPdfBackground_mgg_{etaMed;noConv;noJets}
RooAddPdfBackground_cosThStar_{etaMed;noConv;noJets} RooGaussianBackground_bump_cosThStar_1_{etaMed;noConv;noJets}
RooRealVarbkg_csth01_{etaMed;noJets}
RooRealVarbkg_csthSigma1_{etaMed;noJets} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaMed;noConv;noJets}
RooRealVarbkg_csthPower_{etaMed;noJets}
RooRealVarbkg_csthCoef1_{etaMed;noJets}
RooRealVarbkg_csthCoef2_{etaMed;noJets}
RooRealVarbkg_csthCoef4_{etaMed;noJets}
RooRealVarbkg_csthCoef6_{etaMed;noJets}
RooRealVarbkg_csthRelNorm1_{etaMed;noJets} RooAddPdfBackground_pT_{etaMed;noConv;noJets}
Hfitter::HftPolyExpBackground_pT_2_{etaMed;noConv;noJets}
Hfitter::HftPolyExpBackground_pT_1_{etaMed;noConv;noJets} RooProdPdfSignal_{etaMed;noConv;noJets} RooAddPdfSignal_mgg_{etaMed;noConv;noJets}
RooCBShapeSignal_mgg_Peak_{etaMed;noConv;noJets}
RooFormulaVarmHiggsFormula_{etaMed;noConv;noJets}
RooRealVardmHiggs_{etaMed;noConv} RooRealVarmRes_{etaMed;noConv}
RooRealVartailAlpha_{etaMed;noConv}
RooRealVartailN_{etaMed;noConv}
RooRealVarmggRelNorm_{etaMed;noConv} RooAddPdfSignal_cosThStar_{etaMed;noConv;noJets}RooGaussianSignalcsthstr_1_{etaMed;noConv;noJets}
RooRealVarsig_csthstr0_1_{etaMed;noJets} RooGaussianSignalcsthstr_2_{etaMed;noConv;noJets}
RooRealVarsig_csthstr0_2_{etaMed;noJets} RooRealVarsig_csthstrRelNorm_1_{etaMed;noJets}
RooAddPdfSignal_pT_{etaMed;noConv;noJets}
RooBifurGaussSignalpT_1_{etaMed;noConv;noJets}
RooGaussianSignalpT_2_{etaMed;noConv;noJets}
RooGaussianSignalpT_3_{etaMed;noConv;noJets} RooRealVarn_Background_{etaMed;noConv;noJets} RooFormulaVarnCat_Signal_{etaMed;noConv;noJets}_reparametrized_hgg
RooRealVarf_Signal_{etaMed;noConv;noJets}
RooAddPdfsumPdf_{etaBad;noConv;noJets}_reparametrized_hgg RooProdPdfBackground_{etaBad;noConv;noJets} Hfitter::MggBkgPdfBackground_mgg_{etaBad;noConv;noJets}
RooAddPdfBackground_cosThStar_{etaBad;noConv;noJets} RooGaussianBackground_bump_cosThStar_1_{etaBad;noConv;noJets}
RooRealVarbkg_csth01_{etaBad;noJets}
RooRealVarbkg_csthSigma1_{etaBad;noJets} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaBad;noConv;noJets}
RooRealVarbkg_csthPower_{etaBad;noJets}
RooRealVarbkg_csthCoef1_{etaBad;noJets}
RooRealVarbkg_csthCoef2_{etaBad;noJets}
RooRealVarbkg_csthCoef4_{etaBad;noJets}
RooRealVarbkg_csthCoef6_{etaBad;noJets} RooRealVarbkg_csthRelNorm1_{etaBad;noJets}
RooAddPdfBackground_pT_{etaBad;noConv;noJets}
Hfitter::HftPolyExpBackground_pT_2_{etaBad;noConv;noJets}
Hfitter::HftPolyExpBackground_pT_1_{etaBad;noConv;noJets} RooProdPdfSignal_{etaBad;noConv;noJets}
RooAddPdfSignal_mgg_{etaBad;noConv;noJets} RooCBShapeSignal_mgg_Peak_{etaBad;noConv;noJets}
RooFormulaVarmHiggsFormula_{etaBad;noConv;noJets}
RooRealVardmHiggs_{etaBad;noConv} RooRealVarmRes_{etaBad;noConv}
RooRealVartailAlpha_{etaBad;noConv}
RooRealVartailN_{etaBad;noConv}
RooRealVarmggRelNorm_{etaBad;noConv} RooAddPdfSignal_cosThStar_{etaBad;noConv;noJets}RooGaussianSignalcsthstr_1_{etaBad;noConv;noJets}
RooRealVarsig_csthstr0_1_{etaBad;noJets} RooGaussianSignalcsthstr_2_{etaBad;noConv;noJets}
RooRealVarsig_csthstr0_2_{etaBad;noJets} RooRealVarsig_csthstrRelNorm_1_{etaBad;noJets}
RooAddPdfSignal_pT_{etaBad;noConv;noJets}
RooBifurGaussSignalpT_1_{etaBad;noConv;noJets}
RooGaussianSignalpT_2_{etaBad;noConv;noJets}
RooGaussianSignalpT_3_{etaBad;noConv;noJets} RooRealVarn_Background_{etaBad;noConv;noJets}
RooFormulaVarnCat_Signal_{etaBad;noConv;noJets}_reparametrized_hgg
RooRealVarf_Signal_{etaBad;noConv;noJets}
RooAddPdfsumPdf_{etaGood;Conv;noJets}_reparametrized_hgg RooProdPdfBackground_{etaGood;Conv;noJets} Hfitter::MggBkgPdfBackground_mgg_{etaGood;Conv;noJets}
RooAddPdfBackground_cosThStar_{etaGood;Conv;noJets}
RooGaussianBackground_bump_cosThStar_1_{etaGood;Conv;noJets}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaGood;Conv;noJets}
RooAddPdfBackground_pT_{etaGood;Conv;noJets}
Hfitter::HftPolyExpBackground_pT_2_{etaGood;Conv;noJets}
Hfitter::HftPolyExpBackground_pT_1_{etaGood;Conv;noJets} RooProdPdfSignal_{etaGood;Conv;noJets}
RooAddPdfSignal_mgg_{etaGood;Conv;noJets} RooCBShapeSignal_mgg_Peak_{etaGood;Conv;noJets}
RooFormulaVarmHiggsFormula_{etaGood;Conv;noJets}
RooRealVardmHiggs_{etaGood;Conv} RooRealVarmRes_{etaGood;Conv}
RooRealVartailAlpha_{etaGood;Conv}
RooRealVartailN_{etaGood;Conv} RooRealVarmggRelNorm_{etaGood;Conv}
RooAddPdfSignal_cosThStar_{etaGood;Conv;noJets}RooGaussianSignalcsthstr_1_{etaGood;Conv;noJets}
RooGaussianSignalcsthstr_2_{etaGood;Conv;noJets}
RooAddPdfSignal_pT_{etaGood;Conv;noJets}
RooBifurGaussSignalpT_1_{etaGood;Conv;noJets}
RooGaussianSignalpT_2_{etaGood;Conv;noJets}
RooGaussianSignalpT_3_{etaGood;Conv;noJets} RooRealVarn_Background_{etaGood;Conv;noJets}RooFormulaVarnCat_Signal_{etaGood;Conv;noJets}_reparametrized_hgg
RooRealVarf_Signal_{etaGood;Conv;noJets}
RooAddPdfsumPdf_{etaMed;Conv;noJets}_reparametrized_hgg RooProdPdfBackground_{etaMed;Conv;noJets} Hfitter::MggBkgPdfBackground_mgg_{etaMed;Conv;noJets}
RooAddPdfBackground_cosThStar_{etaMed;Conv;noJets}
RooGaussianBackground_bump_cosThStar_1_{etaMed;Conv;noJets}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaMed;Conv;noJets}
RooAddPdfBackground_pT_{etaMed;Conv;noJets}
Hfitter::HftPolyExpBackground_pT_2_{etaMed;Conv;noJets}
Hfitter::HftPolyExpBackground_pT_1_{etaMed;Conv;noJets} RooProdPdfSignal_{etaMed;Conv;noJets}
RooAddPdfSignal_mgg_{etaMed;Conv;noJets}RooCBShapeSignal_mgg_Peak_{etaMed;Conv;noJets}
RooFormulaVarmHiggsFormula_{etaMed;Conv;noJets}
RooRealVardmHiggs_{etaMed;Conv} RooRealVarmRes_{etaMed;Conv}
RooRealVartailAlpha_{etaMed;Conv}
RooRealVartailN_{etaMed;Conv} RooRealVarmggRelNorm_{etaMed;Conv}RooAddPdfSignal_cosThStar_{etaMed;Conv;noJets}
RooGaussianSignalcsthstr_1_{etaMed;Conv;noJets}
RooGaussianSignalcsthstr_2_{etaMed;Conv;noJets}
RooAddPdfSignal_pT_{etaMed;Conv;noJets}
RooBifurGaussSignalpT_1_{etaMed;Conv;noJets}
RooGaussianSignalpT_2_{etaMed;Conv;noJets}
RooGaussianSignalpT_3_{etaMed;Conv;noJets} RooRealVarn_Background_{etaMed;Conv;noJets}RooFormulaVarnCat_Signal_{etaMed;Conv;noJets}_reparametrized_hgg
RooRealVarf_Signal_{etaMed;Conv;noJets}
RooAddPdfsumPdf_{etaBad;Conv;noJets}_reparametrized_hgg RooProdPdfBackground_{etaBad;Conv;noJets} Hfitter::MggBkgPdfBackground_mgg_{etaBad;Conv;noJets}
RooAddPdfBackground_cosThStar_{etaBad;Conv;noJets} RooGaussianBackground_bump_cosThStar_1_{etaBad;Conv;noJets}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaBad;Conv;noJets}
RooAddPdfBackground_pT_{etaBad;Conv;noJets}
Hfitter::HftPolyExpBackground_pT_2_{etaBad;Conv;noJets}
Hfitter::HftPolyExpBackground_pT_1_{etaBad;Conv;noJets} RooProdPdfSignal_{etaBad;Conv;noJets} RooAddPdfSignal_mgg_{etaBad;Conv;noJets}
RooCBShapeSignal_mgg_Peak_{etaBad;Conv;noJets}
RooFormulaVarmHiggsFormula_{etaBad;Conv;noJets}
RooRealVardmHiggs_{etaBad;Conv} RooRealVarmRes_{etaBad;Conv}
RooRealVartailAlpha_{etaBad;Conv}
RooRealVartailN_{etaBad;Conv} RooRealVarmggRelNorm_{etaBad;Conv}
RooAddPdfSignal_cosThStar_{etaBad;Conv;noJets}RooGaussianSignalcsthstr_1_{etaBad;Conv;noJets}
RooGaussianSignalcsthstr_2_{etaBad;Conv;noJets}
RooAddPdfSignal_pT_{etaBad;Conv;noJets}
RooBifurGaussSignalpT_1_{etaBad;Conv;noJets}
RooGaussianSignalpT_2_{etaBad;Conv;noJets}
RooGaussianSignalpT_3_{etaBad;Conv;noJets} RooRealVarn_Background_{etaBad;Conv;noJets}
RooFormulaVarnCat_Signal_{etaBad;Conv;noJets}_reparametrized_hgg RooRealVarf_Signal_{etaBad;Conv;noJets}
RooAddPdfsumPdf_{etaGood;noConv;jet}_reparametrized_hgg RooProdPdfBackground_{etaGood;noConv;jet} Hfitter::MggBkgPdfBackground_mgg_{etaGood;noConv;jet}
RooRealVarxi_jet RooAddPdfBackground_cosThStar_{etaGood;noConv;jet} RooGaussianBackground_bump_cosThStar_1_{etaGood;noConv;jet}
RooRealVarbkg_csth01_{etaGood;jet}
RooRealVarbkg_csthSigma1_{etaGood;jet} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaGood;noConv;jet}
RooRealVarbkg_csthRelNorm1_{etaGood;jet}
RooAddPdfBackground_pT_{etaGood;noConv;jet}
Hfitter::HftPolyExpBackground_pT_2_{etaGood;noConv;jet}
Hfitter::HftPolyExpBackground_pT_1_{etaGood;noConv;jet}
RooRealVarbkg_ptPow1_jet
RooRealVarbkg_ptExp1_jet RooProdPdfSignal_{etaGood;noConv;jet}RooAddPdfSignal_mgg_{etaGood;noConv;jet}
RooCBShapeSignal_mgg_Peak_{etaGood;noConv;jet}
RooFormulaVarmHiggsFormula_{etaGood;noConv;jet} RooAddPdfSignal_cosThStar_{etaGood;noConv;jet}RooGaussianSignalcsthstr_1_{etaGood;noConv;jet}
RooGaussianSignalcsthstr_2_{etaGood;noConv;jet}
RooAddPdfSignal_pT_{etaGood;noConv;jet}
RooBifurGaussSignalpT_1_{etaGood;noConv;jet}
RooRealVarsig_pt0_1_jet
RooRealVarsig_ptSigmaL_1_jet
RooRealVarsig_ptSigmaR_1_jet RooGaussianSignalpT_2_{etaGood;noConv;jet}
RooRealVarsig_pt0_2_jet
RooRealVarsig_ptSigma_2_jet RooGaussianSignalpT_3_{etaGood;noConv;jet}
RooRealVarsig_pt0_3_jet
RooRealVarsig_ptSigma_3_jet RooRealVarsig_ptRelNorm_1_jet
RooRealVarsig_ptRelNorm_2_jet RooRealVarn_Background_{etaGood;noConv;jet}
RooFormulaVarnCat_Signal_{etaGood;noConv;jet}_reparametrized_hgg
RooRealVarf_Signal_{etaGood;noConv;jet}
RooAddPdfsumPdf_{etaMed;noConv;jet}_reparametrized_hgg RooProdPdfBackground_{etaMed;noConv;jet} Hfitter::MggBkgPdfBackground_mgg_{etaMed;noConv;jet}
RooAddPdfBackground_cosThStar_{etaMed;noConv;jet} RooGaussianBackground_bump_cosThStar_1_{etaMed;noConv;jet}
RooRealVarbkg_csth01_{etaMed;jet}
RooRealVarbkg_csthSigma1_{etaMed;jet} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaMed;noConv;jet}
RooRealVarbkg_csthPower_{etaMed;jet}
RooRealVarbkg_csthCoef1_{etaMed;jet}
RooRealVarbkg_csthCoef2_{etaMed;jet}
RooRealVarbkg_csthCoef4_{etaMed;jet}
RooRealVarbkg_csthCoef6_{etaMed;jet} RooRealVarbkg_csthRelNorm1_{etaMed;jet}
RooAddPdfBackground_pT_{etaMed;noConv;jet}
Hfitter::HftPolyExpBackground_pT_2_{etaMed;noConv;jet}
Hfitter::HftPolyExpBackground_pT_1_{etaMed;noConv;jet} RooProdPdfSignal_{etaMed;noConv;jet} RooAddPdfSignal_mgg_{etaMed;noConv;jet}
RooCBShapeSignal_mgg_Peak_{etaMed;noConv;jet}
RooFormulaVarmHiggsFormula_{etaMed;noConv;jet} RooAddPdfSignal_cosThStar_{etaMed;noConv;jet} RooGaussianSignalcsthstr_1_{etaMed;noConv;jet}
RooRealVarsig_csthstr0_1_{etaMed;jet} RooGaussianSignalcsthstr_2_{etaMed;noConv;jet}
RooRealVarsig_csthstr0_2_{etaMed;jet} RooRealVarsig_csthstrRelNorm_1_{etaMed;jet}
RooAddPdfSignal_pT_{etaMed;noConv;jet}
RooBifurGaussSignalpT_1_{etaMed;noConv;jet}
RooGaussianSignalpT_2_{etaMed;noConv;jet}
RooGaussianSignalpT_3_{etaMed;noConv;jet} RooRealVarn_Background_{etaMed;noConv;jet}
RooFormulaVarnCat_Signal_{etaMed;noConv;jet}_reparametrized_hggRooRealVarf_Signal_{etaMed;noConv;jet}
RooAddPdfsumPdf_{etaBad;noConv;jet}_reparametrized_hgg
RooProdPdfBackground_{etaBad;noConv;jet} Hfitter::MggBkgPdfBackground_mgg_{etaBad;noConv;jet}
RooAddPdfBackground_cosThStar_{etaBad;noConv;jet} RooGaussianBackground_bump_cosThStar_1_{etaBad;noConv;jet}
RooRealVarbkg_csth01_{etaBad;jet}
RooRealVarbkg_csthSigma1_{etaBad;jet} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaBad;noConv;jet}
RooRealVarbkg_csthPower_{etaBad;jet}
RooRealVarbkg_csthCoef1_{etaBad;jet}
RooRealVarbkg_csthCoef2_{etaBad;jet}
RooRealVarbkg_csthCoef4_{etaBad;jet}
RooRealVarbkg_csthCoef6_{etaBad;jet} RooRealVarbkg_csthRelNorm1_{etaBad;jet}
RooAddPdfBackground_pT_{etaBad;noConv;jet}
Hfitter::HftPolyExpBackground_pT_2_{etaBad;noConv;jet}
Hfitter::HftPolyExpBackground_pT_1_{etaBad;noConv;jet} RooProdPdfSignal_{etaBad;noConv;jet}RooAddPdfSignal_mgg_{etaBad;noConv;jet}
RooCBShapeSignal_mgg_Peak_{etaBad;noConv;jet}
RooFormulaVarmHiggsFormula_{etaBad;noConv;jet} RooAddPdfSignal_cosThStar_{etaBad;noConv;jet}RooGaussianSignalcsthstr_1_{etaBad;noConv;jet}
RooRealVarsig_csthstr0_1_{etaBad;jet} RooGaussianSignalcsthstr_2_{etaBad;noConv;jet}
RooRealVarsig_csthstr0_2_{etaBad;jet} RooRealVarsig_csthstrRelNorm_1_{etaBad;jet}
RooAddPdfSignal_pT_{etaBad;noConv;jet}
RooBifurGaussSignalpT_1_{etaBad;noConv;jet}
RooGaussianSignalpT_2_{etaBad;noConv;jet}
RooGaussianSignalpT_3_{etaBad;noConv;jet} RooRealVarn_Background_{etaBad;noConv;jet} RooFormulaVarnCat_Signal_{etaBad;noConv;jet}_reparametrized_hgg
RooRealVarf_Signal_{etaBad;noConv;jet}
RooAddPdfsumPdf_{etaMed;Conv;jet}_reparametrized_hgg RooProdPdfBackground_{etaMed;Conv;jet} Hfitter::MggBkgPdfBackground_mgg_{etaMed;Conv;jet}
RooAddPdfBackground_cosThStar_{etaMed;Conv;jet} RooGaussianBackground_bump_cosThStar_1_{etaMed;Conv;jet}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaMed;Conv;jet}
RooAddPdfBackground_pT_{etaMed;Conv;jet}
Hfitter::HftPolyExpBackground_pT_2_{etaMed;Conv;jet}
Hfitter::HftPolyExpBackground_pT_1_{etaMed;Conv;jet} RooProdPdfSignal_{etaMed;Conv;jet}
RooAddPdfSignal_mgg_{etaMed;Conv;jet}RooCBShapeSignal_mgg_Peak_{etaMed;Conv;jet}
RooFormulaVarmHiggsFormula_{etaMed;Conv;jet} RooAddPdfSignal_cosThStar_{etaMed;Conv;jet}RooGaussianSignalcsthstr_1_{etaMed;Conv;jet}
RooGaussianSignalcsthstr_2_{etaMed;Conv;jet}
RooAddPdfSignal_pT_{etaMed;Conv;jet}
RooBifurGaussSignalpT_1_{etaMed;Conv;jet}
RooGaussianSignalpT_2_{etaMed;Conv;jet}
RooGaussianSignalpT_3_{etaMed;Conv;jet} RooRealVarn_Background_{etaMed;Conv;jet}
RooFormulaVarnCat_Signal_{etaMed;Conv;jet}_reparametrized_hgg
RooRealVarf_Signal_{etaMed;Conv;jet}
RooAddPdfsumPdf_{etaBad;Conv;jet}_reparametrized_hgg RooProdPdfBackground_{etaBad;Conv;jet} Hfitter::MggBkgPdfBackground_mgg_{etaBad;Conv;jet}
RooAddPdfBackground_cosThStar_{etaBad;Conv;jet} RooGaussianBackground_bump_cosThStar_1_{etaBad;Conv;jet}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaBad;Conv;jet}
RooAddPdfBackground_pT_{etaBad;Conv;jet}
Hfitter::HftPolyExpBackground_pT_2_{etaBad;Conv;jet}
Hfitter::HftPolyExpBackground_pT_1_{etaBad;Conv;jet} RooProdPdfSignal_{etaBad;Conv;jet}
RooAddPdfSignal_mgg_{etaBad;Conv;jet}RooCBShapeSignal_mgg_Peak_{etaBad;Conv;jet}
RooFormulaVarmHiggsFormula_{etaBad;Conv;jet} RooAddPdfSignal_cosThStar_{etaBad;Conv;jet}RooGaussianSignalcsthstr_1_{etaBad;Conv;jet}
RooGaussianSignalcsthstr_2_{etaBad;Conv;jet}
RooAddPdfSignal_pT_{etaBad;Conv;jet}
RooBifurGaussSignalpT_1_{etaBad;Conv;jet}
RooGaussianSignalpT_2_{etaBad;Conv;jet}
RooGaussianSignalpT_3_{etaBad;Conv;jet} RooRealVarn_Background_{etaBad;Conv;jet}
RooFormulaVarnCat_Signal_{etaBad;Conv;jet}_reparametrized_hgg
RooRealVarf_Signal_{etaBad;Conv;jet}
RooAddPdfsumPdf_{etaGood;noConv;vbf}_reparametrized_hgg RooProdPdfBackground_{etaGood;noConv;vbf} Hfitter::MggBkgPdfBackground_mgg_{etaGood;noConv;vbf}
RooRealVarxi_vbf RooAddPdfBackground_cosThStar_{etaGood;noConv;vbf}
RooGaussianBackground_bump_cosThStar_1_{etaGood;noConv;vbf}RooRealVarbkg_csth01_{etaGood;vbf}
RooRealVarbkg_csthSigma1_{etaGood;vbf} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaGood;noConv;vbf}
RooRealVarbkg_csthPower_{etaGood;vbf}
RooRealVarbkg_csthCoef1_{etaGood;vbf}
RooRealVarbkg_csthCoef2_{etaGood;vbf}
RooRealVarbkg_csthCoef4_{etaGood;vbf}
RooRealVarbkg_csthCoef6_{etaGood;vbf}
RooRealVarbkg_csthRelNorm1_{etaGood;vbf} RooAddPdfBackground_pT_{etaGood;noConv;vbf}
Hfitter::HftPolyExpBackground_pT_2_{etaGood;noConv;vbf}
RooRealVarbkg_ptPow2_vbf
RooRealVarbkg_ptExp2_vbf Hfitter::HftPolyExpBackground_pT_1_{etaGood;noConv;vbf}
RooRealVarbkg_ptPow1_vbf
RooRealVarbkg_ptExp1_vbf RooRealVarbkg_ptTail1Norm_vbf RooProdPdfSignal_{etaGood;noConv;vbf}RooAddPdfSignal_mgg_{etaGood;noConv;vbf}
RooCBShapeSignal_mgg_Peak_{etaGood;noConv;vbf}
RooFormulaVarmHiggsFormula_{etaGood;noConv;vbf} RooAddPdfSignal_cosThStar_{etaGood;noConv;vbf}RooGaussianSignalcsthstr_1_{etaGood;noConv;vbf}
RooRealVarsig_csthstr0_1_{etaGood;vbf} RooGaussianSignalcsthstr_2_{etaGood;noConv;vbf}
RooRealVarsig_csthstr0_2_{etaGood;vbf} RooRealVarsig_csthstrRelNorm_1_{etaGood;vbf}
RooAddPdfSignal_pT_{etaGood;noConv;vbf}
RooBifurGaussSignalpT_1_{etaGood;noConv;vbf}
RooRealVarsig_pt0_1_vbf
RooRealVarsig_ptSigmaL_1_vbf
RooRealVarsig_ptSigmaR_1_vbf RooGaussianSignalpT_2_{etaGood;noConv;vbf}
RooRealVarsig_pt0_2_vbf
RooRealVarsig_ptSigma_2_vbf RooGaussianSignalpT_3_{etaGood;noConv;vbf}
RooRealVarsig_pt0_3_vbf
RooRealVarsig_ptSigma_3_vbf RooRealVarsig_ptRelNorm_1_vbf
RooRealVarsig_ptRelNorm_2_vbf RooRealVarn_Background_{etaGood;noConv;vbf}
RooFormulaVarnCat_Signal_{etaGood;noConv;vbf}_reparametrized_hggRooRealVarf_Signal_{etaGood;noConv;vbf}
RooAddPdfsumPdf_{etaMed;noConv;vbf}_reparametrized_hgg RooProdPdfBackground_{etaMed;noConv;vbf} Hfitter::MggBkgPdfBackground_mgg_{etaMed;noConv;vbf}
RooAddPdfBackground_cosThStar_{etaMed;noConv;vbf}
RooGaussianBackground_bump_cosThStar_1_{etaMed;noConv;vbf}
RooRealVarbkg_csth01_{etaMed;vbf}
RooRealVarbkg_csthSigma1_{etaMed;vbf} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaMed;noConv;vbf}
RooRealVarbkg_csthPower_{etaMed;vbf}
RooRealVarbkg_csthCoef1_{etaMed;vbf}
RooRealVarbkg_csthCoef2_{etaMed;vbf}
RooRealVarbkg_csthCoef4_{etaMed;vbf}
RooRealVarbkg_csthCoef6_{etaMed;vbf}
RooRealVarbkg_csthRelNorm1_{etaMed;vbf} RooAddPdfBackground_pT_{etaMed;noConv;vbf}
Hfitter::HftPolyExpBackground_pT_2_{etaMed;noConv;vbf}
Hfitter::HftPolyExpBackground_pT_1_{etaMed;noConv;vbf} RooProdPdfSignal_{etaMed;noConv;vbf}RooAddPdfSignal_mgg_{etaMed;noConv;vbf}
RooCBShapeSignal_mgg_Peak_{etaMed;noConv;vbf}
RooFormulaVarmHiggsFormula_{etaMed;noConv;vbf} RooAddPdfSignal_cosThStar_{etaMed;noConv;vbf}RooGaussianSignalcsthstr_1_{etaMed;noConv;vbf}
RooRealVarsig_csthstr0_1_{etaMed;vbf} RooGaussianSignalcsthstr_2_{etaMed;noConv;vbf}
RooRealVarsig_csthstr0_2_{etaMed;vbf} RooRealVarsig_csthstrRelNorm_1_{etaMed;vbf}
RooAddPdfSignal_pT_{etaMed;noConv;vbf}
RooBifurGaussSignalpT_1_{etaMed;noConv;vbf}
RooGaussianSignalpT_2_{etaMed;noConv;vbf}
RooGaussianSignalpT_3_{etaMed;noConv;vbf} RooRealVarn_Background_{etaMed;noConv;vbf}
RooFormulaVarnCat_Signal_{etaMed;noConv;vbf}_reparametrized_hggRooRealVarf_Signal_{etaMed;noConv;vbf}
RooAddPdfsumPdf_{etaBad;noConv;vbf}_reparametrized_hgg RooProdPdfBackground_{etaBad;noConv;vbf} Hfitter::MggBkgPdfBackground_mgg_{etaBad;noConv;vbf}
RooAddPdfBackground_cosThStar_{etaBad;noConv;vbf}
RooGaussianBackground_bump_cosThStar_1_{etaBad;noConv;vbf}
RooRealVarbkg_csth01_{etaBad;vbf}
RooRealVarbkg_csthSigma1_{etaBad;vbf} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaBad;noConv;vbf}
RooRealVarbkg_csthPower_{etaBad;vbf}
RooRealVarbkg_csthCoef1_{etaBad;vbf}
RooRealVarbkg_csthCoef2_{etaBad;vbf}
RooRealVarbkg_csthCoef4_{etaBad;vbf}
RooRealVarbkg_csthCoef6_{etaBad;vbf}
RooRealVarbkg_csthRelNorm1_{etaBad;vbf} RooAddPdfBackground_pT_{etaBad;noConv;vbf}
Hfitter::HftPolyExpBackground_pT_2_{etaBad;noConv;vbf}
Hfitter::HftPolyExpBackground_pT_1_{etaBad;noConv;vbf} RooProdPdfSignal_{etaBad;noConv;vbf} RooAddPdfSignal_mgg_{etaBad;noConv;vbf}
RooCBShapeSignal_mgg_Peak_{etaBad;noConv;vbf}
RooFormulaVarmHiggsFormula_{etaBad;noConv;vbf} RooAddPdfSignal_cosThStar_{etaBad;noConv;vbf}RooGaussianSignalcsthstr_1_{etaBad;noConv;vbf}
RooRealVarsig_csthstr0_1_{etaBad;vbf} RooGaussianSignalcsthstr_2_{etaBad;noConv;vbf}
RooRealVarsig_csthstr0_2_{etaBad;vbf} RooRealVarsig_csthstrRelNorm_1_{etaBad;vbf}
RooAddPdfSignal_pT_{etaBad;noConv;vbf}
RooBifurGaussSignalpT_1_{etaBad;noConv;vbf}
RooGaussianSignalpT_2_{etaBad;noConv;vbf}
RooGaussianSignalpT_3_{etaBad;noConv;vbf} RooRealVarn_Background_{etaBad;noConv;vbf}RooFormulaVarnCat_Signal_{etaBad;noConv;vbf}_reparametrized_hgg
RooRealVarf_Signal_{etaBad;noConv;vbf}
RooAddPdfsumPdf_{etaGood;Conv;vbf}_reparametrized_hgg RooProdPdfBackground_{etaGood;Conv;vbf} Hfitter::MggBkgPdfBackground_mgg_{etaGood;Conv;vbf}
RooAddPdfBackground_cosThStar_{etaGood;Conv;vbf}
RooGaussianBackground_bump_cosThStar_1_{etaGood;Conv;vbf} Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaGood;Conv;vbf}
RooAddPdfBackground_pT_{etaGood;Conv;vbf}
Hfitter::HftPolyExpBackground_pT_2_{etaGood;Conv;vbf}
Hfitter::HftPolyExpBackground_pT_1_{etaGood;Conv;vbf} RooProdPdfSignal_{etaGood;Conv;vbf} RooAddPdfSignal_mgg_{etaGood;Conv;vbf}
RooCBShapeSignal_mgg_Peak_{etaGood;Conv;vbf}
RooFormulaVarmHiggsFormula_{etaGood;Conv;vbf} RooAddPdfSignal_cosThStar_{etaGood;Conv;vbf}RooGaussianSignalcsthstr_1_{etaGood;Conv;vbf}
RooGaussianSignalcsthstr_2_{etaGood;Conv;vbf}
RooAddPdfSignal_pT_{etaGood;Conv;vbf}
RooBifurGaussSignalpT_1_{etaGood;Conv;vbf}
RooGaussianSignalpT_2_{etaGood;Conv;vbf}
RooGaussianSignalpT_3_{etaGood;Conv;vbf} RooRealVarn_Background_{etaGood;Conv;vbf}
RooFormulaVarnCat_Signal_{etaGood;Conv;vbf}_reparametrized_hggRooRealVarf_Signal_{etaGood;Conv;vbf}
RooAddPdfsumPdf_{etaMed;Conv;vbf}_reparametrized_hgg RooProdPdfBackground_{etaMed;Conv;vbf}Hfitter::MggBkgPdfBackground_mgg_{etaMed;Conv;vbf}
RooAddPdfBackground_cosThStar_{etaMed;Conv;vbf}
RooGaussianBackground_bump_cosThStar_1_{etaMed;Conv;vbf}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaMed;Conv;vbf}
RooAddPdfBackground_pT_{etaMed;Conv;vbf}
Hfitter::HftPolyExpBackground_pT_2_{etaMed;Conv;vbf}
Hfitter::HftPolyExpBackground_pT_1_{etaMed;Conv;vbf} RooProdPdfSignal_{etaMed;Conv;vbf}
RooAddPdfSignal_mgg_{etaMed;Conv;vbf}
RooCBShapeSignal_mgg_Peak_{etaMed;Conv;vbf}
RooFormulaVarmHiggsFormula_{etaMed;Conv;vbf} RooAddPdfSignal_cosThStar_{etaMed;Conv;vbf}
RooGaussianSignalcsthstr_1_{etaMed;Conv;vbf}
RooGaussianSignalcsthstr_2_{etaMed;Conv;vbf}
RooAddPdfSignal_pT_{etaMed;Conv;vbf}
RooBifurGaussSignalpT_1_{etaMed;Conv;vbf}
RooGaussianSignalpT_2_{etaMed;Conv;vbf}
RooGaussianSignalpT_3_{etaMed;Conv;vbf} RooRealVarn_Background_{etaMed;Conv;vbf}
RooFormulaVarnCat_Signal_{etaMed;Conv;vbf}_reparametrized_hggRooRealVarf_Signal_{etaMed;Conv;vbf}
RooAddPdfsumPdf_{etaBad;Conv;vbf}_reparametrized_hgg RooProdPdfBackground_{etaBad;Conv;vbf} Hfitter::MggBkgPdfBackground_mgg_{etaBad;Conv;vbf}
RooAddPdfBackground_cosThStar_{etaBad;Conv;vbf}
RooGaussianBackground_bump_cosThStar_1_{etaBad;Conv;vbf}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaBad;Conv;vbf}
RooAddPdfBackground_pT_{etaBad;Conv;vbf}
Hfitter::HftPolyExpBackground_pT_2_{etaBad;Conv;vbf}
Hfitter::HftPolyExpBackground_pT_1_{etaBad;Conv;vbf} RooProdPdfSignal_{etaBad;Conv;vbf}RooAddPdfSignal_mgg_{etaBad;Conv;vbf}
RooCBShapeSignal_mgg_Peak_{etaBad;Conv;vbf}
RooFormulaVarmHiggsFormula_{etaBad;Conv;vbf} RooAddPdfSignal_cosThStar_{etaBad;Conv;vbf}
RooGaussianSignalcsthstr_1_{etaBad;Conv;vbf}
RooGaussianSignalcsthstr_2_{etaBad;Conv;vbf}
RooAddPdfSignal_pT_{etaBad;Conv;vbf}
RooBifurGaussSignalpT_1_{etaBad;Conv;vbf}
RooGaussianSignalpT_2_{etaBad;Conv;vbf}
RooGaussianSignalpT_3_{etaBad;Conv;vbf} RooRealVarn_Background_{etaBad;Conv;vbf}
RooFormulaVarnCat_Signal_{etaBad;Conv;vbf}_reparametrized_hgg
RooRealVarf_Signal_{etaBad;Conv;vbf} RooRealVarbkg_csthPower_{etaGood;jet}
RooRealVarbkg_csthCoef1_{etaGood;jet}
RooRealVarbkg_csthCoef2_{etaGood;jet}
RooRealVarbkg_csthCoef4_{etaGood;jet}
RooRealVarbkg_csthCoef6_{etaGood;jet}
RooRealVarbkg_ptPow2_jet
RooRealVarbkg_ptExp2_jet RooRealVarbkg_ptTail1Norm_jet
RooRealVarsig_csthstr0_1_{etaGood;jet}
RooRealVarsig_csthstr0_2_{etaGood;jet} RooRealVarsig_csthstrRelNorm_1_{etaGood;jet}
RooAddPdfsumPdf_{etaGood;Conv;jet}_reparametrized_hgg RooProdPdfBackground_{etaGood;Conv;jet} Hfitter::MggBkgPdfBackground_mgg_{etaGood;Conv;jet}
RooAddPdfBackground_cosThStar_{etaGood;Conv;jet} RooGaussianBackground_bump_cosThStar_1_{etaGood;Conv;jet}
Hfitter::HftPeggedPolyBackground_poly_cosThStar_{etaGood;Conv;jet}
RooAddPdfBackground_pT_{etaGood;Conv;jet}
Hfitter::HftPolyExpBackground_pT_2_{etaGood;Conv;jet}
Hfitter::HftPolyExpBackground_pT_1_{etaGood;Conv;jet} RooProdPdfSignal_{etaGood;Conv;jet}RooAddPdfSignal_mgg_{etaGood;Conv;jet}
RooCBShapeSignal_mgg_Peak_{etaGood;Conv;jet}
RooFormulaVarmHiggsFormula_{etaGood;Conv;jet} RooAddPdfSignal_cosThStar_{etaGood;Conv;jet}
RooGaussianSignalcsthstr_1_{etaGood;Conv;jet}
RooGaussianSignalcsthstr_2_{etaGood;Conv;jet}
RooAddPdfSignal_pT_{etaGood;Conv;jet}
RooBifurGaussSignalpT_1_{etaGood;Conv;jet}
RooGaussianSignalpT_2_{etaGood;Conv;jet}
RooGaussianSignalpT_3_{etaGood;Conv;jet} RooRealVarn_Background_{etaGood;Conv;jet}
RooFormulaVarnCat_Signal_{etaGood;Conv;jet}_reparametrized_hggRooRealVarf_Signal_{etaGood;Conv;jet}