Boosting $H\to b\bar b$ with Machine Learning

TL;DR

This paper introduces a two-stream convolutional neural network to improve the detection of boosted Higgs bosons decaying to b-quarks at hadron colliders, significantly enhancing discovery potential amid large QCD backgrounds.

Contribution

The study develops a novel neural network architecture combining jet and event information, surpassing current methods in identifying boosted Higgs signals and exploring sources of additional discrimination power.

Findings

Neural network increases Higgs discovery sensitivity.

Double b-tagging effectively reduces background.

Method applicable to other final states.

Abstract

High $p_T$ Higgs production at hadron colliders provides a direct probe of the internal structure of the $gg \to H$ loop with the $H \to b\bar{b}$ decay offering the most statistics due to the large branching ratio. Despite the overwhelming QCD background, recent advances in jet substructure have put the observation of the $gg\to H \to b\bar{b}$ channel at the LHC within the realm of possibility. In order to enhance the sensitivity to this process, we develop a two stream convolutional neural network, with one stream acting on jet information and one using global event properties. The neural network significantly increases the discovery potential of a Higgs signal, both for high $p_T$ Standard Model production as well for possible beyond the Standard Model contributions. Unlike most studies for boosted hadronically decaying massive particles, the boosted Higgs search is unique because double $b$-tagging rejects nearly all background processes that do not have two hard prongs. In this context --- which goes beyond state-of-the-art two-prong tagging --- the network is studied to identify the origin of the additional information leading to the increased significance. The procedures described here are also applicable to related final states where they can be used to identify additional sources of discrimination power that are not being exploited by current techniques.