IN2P3/IRFU Machine Learning workshop

Deep learning in ATLAS ttH(H->bb) analysis

23 janv. 2020, 11:20

25m

Amphi (CC-IN2P3)

ML for analysis : Application of Machine Learning to analysis, event classification and fundamental parameters inference

Orateur

Yann Coadou (CPPM, Aix-Marseille Université, CNRS/IN2P3)

Description

The associated ttH production was observed at the LHC in 2018, mostly driven by the multilepton and gammagamma final states. The H->bb final state remains so far elusive. The latest results from ATLAS in this channel rely on boosted decision trees, used to assign jets to partons and to separate the ttH signal from the main ttbar+HF background. In this talk several new ways to tackle this challenging analysis are investigated, using RNN based on jet combinations, testing the performance with low-level variables rather than physics-motivated ones, and integrating physics knowledge into the learning procedure via parse trees.

Documents de présentation

ttHbb_IN2P3_ML_WS200123.pdf