Identification of boosted Higgs bosons decaying into b-quark pairs with the ATLAS detector at 13 $$\text {TeV}$$

Abstract

This paper describes a study of techniques for identifying Higgs bosons at high transverse momenta decaying into bottom-quark pairs, H → bb, for proton-proton collision data collected by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energy √ s = 13 TeV. These decays are reconstructed from calorimeter jets found with the anti-k t R = 1.0 jet algorithm. To tag Higgs bosons, a combination of requirements is used: b-tagging of R = 0.2 track-jets matched to the largeR calorimeter jet, and requirements on the jet mass and other jet substructure variables. The Higgs boson tagging efficiency and corresponding multijet and hadronic top-quark background rejections are evaluated using Monte Carlo simulation. Several benchmark tagging selections are defined for different signal efficiency targets. The modelling of the relevant input distributions used to tag Higgs bosons is studied in 36 fb −1 of data collected in 2015 and 2016 using g → bb and Z (→ bb)γ event selections in data. Both processes are found to be well modelled within the statistical and systematic uncertainties.

Calculating pull for non-singlet jets

“…Correspondingly, subjet radii for individual bottom quarks of R 0.4 are reasonable as now even down to R = 0.2 is used in experiment [21]. Importantly, for the subjets to be well-defined and non-overlapping, their jet radius R should be less than half of the angular separation of the bottom quarks, θ 12 .…”

Interaction networks for the identification of boosted H→bb¯ decays

“…Other more generic CMS algorithms, also based on deep neural networks and known as the boosted event shape tagger (BEST) and the DeepAK8 tagger, were created to classify the decays of multiple heavy resonances, including H, Z, W, and t [13]. The ATLAS Collaboration has also designed an algorithm to identify two b hadrons within an anti-k T R ¼ 1 jet using b tagging of track-based subjets [14]. For the task of H → bb identification, the CMS DDB tagger, DeepAK8 algorithm, and the ATLAS tagger achieve similar state-ofthe-art performance.…”

“…II), both using expert features with dense layers or raw data representations (e.g., images or lists of particle properties) with more complex architectures. For instance, the LHC collaborations and other researchers have investigated the optimal way to combine substructure, tracking, and vertexing information to enhance the tagging efficiency for high-p T H → bb decays [10][11][12][13][14][15]. This is an important task in particle physics because measurements of high-p T H → bb decays may help resolve the loop-induced and tree-level contributions to the gluon fusion process, providing a complementary approach to study the t Yukawa beyond the ttH process [16][17][18][19].…”

Supervised jet clustering with graph neural networks for Lorentz boosted bosons

“…These methods range from physically motivated features such as groomed jet mass [28][29][30][31][32], N-subjettiness [33,34] and D 2 [35] to complex observables built using machine learning [21]. ATLAS and CMS have integrated and extended these methods as well as studied them using collision data [36][37][38][39][40]. One feature that all of these algorithms have in common is that they start from a collection of constituents selected using a jet clustering algorithm.…”