Transformer Neural Networks in the Measurement of $t\bar{t}H$ Production in the $H\,{\to}\,b\bar{b}$ Decay Channel with ATLAS

TL;DR

This paper demonstrates the application of transformer neural networks to improve the measurement of Higgs boson production in association with top quarks, specifically in the $H\to b\bar{b}$ decay channel, using ATLAS data.

Contribution

It introduces transformer neural networks for signal-background discrimination and Higgs reconstruction, enhancing analysis sensitivity over previous methods.

Findings

Observed a 4.6 sigma excess over background

Achieved improved discrimination of signal and background

Enhanced Higgs transverse momentum reconstruction

Abstract

A measurement of Higgs boson production in association with a top quark pair in the bottom anti-bottom Higgs boson decay channel and leptonic final states is presented. The analysis uses $140\,\mathrm{fb}^{-1}$ of $13\,\mathrm{TeV}$ proton proton collision data collected by the ATLAS detector at the Large Hadron Collider. A particular focus is placed on the role played by transformer neural networks in discriminating signal and background processes via multi-class discriminants and in reconstructing the Higgs boson transverse momentum. These powerful multi-variate analysis techniques significantly improve the analysis over a previous measurement using the same dataset. Overall, an excess of 4.6 (5.4) standard deviations over the background-only hypothesis was observed (expected).