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Quantifying ML uncertainties in searches for new physics at the LHC
par Tristan Beau - 26 octobre 2023
Titre : Quantifying ML uncertainties in searches for new physics at the LHC
Directrice/directeur de thèse : Anja Butter
Co-encadrant.e : Bertrand Laforge
Groupe d’accueil :ATLAS
Description :
Precise estimation of uncertainties is a crucial asset in the search for new physics at the LHC. While neural network based simulation and analysis methods have enable a more efficient treatment of high-dimensional data, a rigorous treatment of network induced uncertainties remains elusive.
In the research project the PhD student will explore different methods to estimate network induced uncertainties. Starting from toy examples that highlight limitations of interpolation and extrapolation the student will analyse the properties of multiple methods including Bayesian Neural Networks, ensemble methods and mutual information. Once advantages and limitations of each method are understood, the student will apply them to complex high energy physics problems like jet unfolding and event simulation.
Lieu(x) de travail : LPNHE
Stage proposé avant la thèse : Oui
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