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The SModelS wiki can be found at https://smodels.github.io/
- analysis combination when
experimentalFeatures = True
:- dataselector = upperLimit / efficiencyMap should choose type of results that enter the combination -- DONE
- otherwise, if dataselector = all, EM results should get precedence in the combination (i.e. when both, EMs and exp. ULs, are available for the same analysis)
From previous todo's (still open)
- combination that runs with missing txname files
- add new xsec computers
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MSSM EW-ino scan
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to what extent can we improve coverage [of mixed scenarios] by combining likelihoods from different analyses?
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need EMs for all the leading EW searches --> recasting
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IDM scenario : add EMs for TChiZ topology from EW SUSY (chargino, neutralino, slepton) and Higgs->inv analyses; can we cover the low mass region with prompt decays?
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Mono-X : how to include mono-X searches in SModelS?
- Study efficiencies as a function of spin and production mode
- Implement conservative case
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Implement simplified pyhf likelihoods --> Gael and Timothee
- create simplified pyhf JSON files with simplify tool, see also here
- compare them against full ones, see ATL-PHYS-PUB-2021-038
- add switch in SModelS to use either full of simplified JSON likelihoods
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Likelihoods from exp+obs upper limits --> WW and Timothee
- implement feature
- use optimized truncation from Leo
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Implement joint likelihoods in SModelS (add feature for analyses correlation matrix)
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Get likelihoods from individual analyses as function of signal strength
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how to build a combined likelihood function to be accessed after theory predictions?
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introduce likelihood object
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Numerical maximization of "heterogeneous" joint likelihoods
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Copula functions may be used to model correlations between analyses
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Generalize statistical procedure to deal with multiple signal strengths
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Revise combination criteria: sqrts, experiment, constraints + info if hadronic or leptonic for topologies with tops; allow commbination of prompt and long-lived
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Aggregation algorithms
Storing and directly using the inverse of the covariance matrix speeds up the code by a factor 10!
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Document aggregation algrorithm and its usage on the database wiki
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Set small correlations to zero:
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do we gain in CPU performance?
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try to split off SRs with small correlations from large covariance matrix to get a smaller cov.M times product of approx. uncorrelated likelihoods
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up to what size may one neglect correlations before loosing in precision?
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try different types of distance measures (min, max, mean) between sets of SRs
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think about using Fisher information for aggregation
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generalize from symmetric to asymmetric uncertainties (variable Gaussian approach, cf Lilith)
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