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Sabine Kraml edited this page Aug 19, 2021 · 23 revisions

The SModelS wiki can be found at https://smodels.github.io/ <https://smodels.github.io/>_

Todo for next SModelS version (2.0.1 or 2.1.0):

  • modelTester mode that returns all datasets
  • validation mode that writes out next-to-best SRs
  • combination that runs with missing txname files
  • add new xsec computers

Student Projects

  • MSSM EW-ino scan

    • to what extent can we improve coverage [of mixed scenarios] by combining likelihoods from different analyses?

    • need EMs for all the leading EW searches --> recasting

  • 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?

  • Mono-X : how to include mono-X searches in SModelS?

    • Study efficiencies as a function of spin and production mode
    • Implement conservative case

  • Implement joint likelihoods in SModelS (add feature for analyses correlation matrix)

    • Get likelihoods from individual analyses as function of signal strength

      • how to build a combined likelihood function to be accessed after theory predictions?

      • introduce likelihood object

    • Numerical maximization of "heterogeneous" joint likelihoods

    • Copula functions may be used to model correlations between analyses

    • Generalize statistical procedure to deal with multiple signal strengths

    • Revise combination criteria: sqrts, experiment, constraints + info if hadronic or leptonic for topologies with tops; allow commbination of prompt and long-lived

  • Aggregation algorithms

    • Set small correlations to zero:

      1. do we gain in CPU performance?

      2. try to split off SRs with small correlations from large covariance matrix to get a smaller cov.M times product of approx. uncorrelated likelihoods

      3. up to what size may one neglect correlations before loosing in precision?

    • try different types of distance measures (min, max, mean) between sets of SRs

    • think about using Fisher information for aggregation

    • generalize from symmetric to asymmetric uncertainties (variable Gaussian approach, cf Lilith)

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