Machine learning techniques for jet reconstruction at LHCb and application to the search for $H \to b \bar{b}$ and $H \to c \bar{c}$ in $\sqrt{s}=13$ TeV $pp$ collisions

TL;DR

This paper introduces machine learning techniques for jet energy calibration and flavor tagging at LHCb, applying them to search for Higgs decays to bottom and charm quarks, setting upper limits on their production cross-sections.

Contribution

It presents novel ML-based methods for jet calibration and flavor tagging at LHCb, enhancing Higgs decay searches in proton-proton collisions.

Findings

Set upper limits on $H \to b\bar{b}$ and $H \to c\bar{c}$ decay rates.

Demonstrated improved jet measurement techniques using ML.

Applied ML methods to real LHCb data for Higgs searches.

Abstract

Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between $b$-quark, $c$-quark, and light parton jets. These techniques are applied to a search for inclusive $H \to \bbbar$ and $H \to c\barcc$ decays using a LHCb dataset corresponding to an integrated luminosity of 1.6\invfb. The observed (expected) 95\% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the $H \to b\bar b$ process, and 1003 (1834) times the SM cross-section for the $H \to c\bar c$ process.