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IRN Terascale @ LPSC Grenoble

Apr 24 – 26, 2023
LPSC Grenoble
Europe/Paris timezone

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Session

Methods and Tools

Apr 26, 2023, 9:30 AM
Grand Amphi (LPSC Grenoble)

Grand Amphi

LPSC Grenoble

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  1. 31. SUSPECT3
    Dirk Zerwas (IJCLab and DMLab)
    4/26/23, 9:30 AM
    Methods and Tools

    Presentation of the new vesrion of SUSPECT3 (v3.1.1) described in "SuSpect3: A C++ Code for the Supersymmetric and Higgs Particle Spectrum of the MSSM"

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  2. 38. New developments in SModelS
    Timothee Pascal
    4/26/23, 9:55 AM
    Methods and Tools

    I will present recent developments in SModelS, in particular the update of the database with the latest available experimental results for full Run-2 luminosity, the interface to the new statistical package Spey, and the statistical combination of analyses. The latter allows one to increase the robustness of the statistically inferred constraints. To demonstrate the physics impact, I will use...

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  3. 32. Simplified likelihoods using linearized systematic uncertainties
    Nicolas Berger (LAPP)
    4/26/23, 10:20 AM
    Methods and Tools

    The ability to reuse published experimental results -- for instance reinterpretations in the context of alternative models, or combinations of multiple results -- is crucial to searches for new phenomena in high energy physics. The information that is made public, typically best-fit values, uncertainties and covariance matrices, is often insufficient to fully carry out this program, in...

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  4. 41. NFLikelihood: Unsupervised Machine Learning LHC likelihoods with Normalizing Flows.
    Humberto Reyes-González (University of Genoa)
    4/26/23, 11:15 AM
    Methods and Tools

    Full statistical models encapsulate the complete information of an experimental result, including the likelihood function given observed data. Their proper publication is of vital importance for a long lasting legacy of the LHC. Major steps have been taken towards this goal; a notable example being ATLAS release of statistical models with the pyhf framework. However, even the likelihoods are...

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  5. 33. Can we trust machine learning? Parton distributions as a case study
    Stefano Forte (Dipartimento di Fisica, Università di Milano)
    4/26/23, 11:40 AM
    Methods and Tools

    I discussed some issues that arise when using Machine Learning as an inference tool in the particular context of the determination of parton distributions. Problems I address include: how do we know that the ML model generalizes correctly? Can we detect overlearning? Can we assign an uncertainty to the ML model predictions, and can we validate this assignment?

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  6. 62. Round table
    4/26/23, 12:05 PM
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