Tagging the Higgs boson decay to bottom quarks with colour-sensitive observables and the Lund jet plane

Abstract

We study the problem of distinguishing $b$-jets stemming from the decay of a colour singlet, such as the Higgs boson, from those originating from the abundant QCD background. In particular, as a case study, we focus on associate production of a vector boson and a Higgs boson decaying into a pair of $b$-jets, which has been recently observed at the LHC. We consider the combination of several theory-driven observables proposed in the literature, together with Lund jet plane images, in order to design an original $Hbb$ tagger. The observables are combined by means of standard machine learning algorithms, which are trained on events obtained with fast detector simulation techniques. We find that the combination of high-level single-variable observables with the Lund jet plane provides an excellent discrimination performance. We also study the dependence of the tagger on the invariant mass of the decaying particles, in order to assess the extension to a generic $Xbb$ tagger.

arXiv comment12 pages, 5 figures, 5 tables. v2 matches published version

References in corpus (47)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Better Jet Clustering Algorithms
• Jet substructure as a new Higgs search channel at the LHC
• Soft Drop
• The Catchment Area of Jets
• Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV
• Energy Correlation Functions for Jet Substructure
• ATLAS $b$-jet identification performance and efficiency measurement with $t\bar{t}$ events in $pp$ collisions at $\sqrt{s}=13$ TeV
• Observation of Higgs boson decay to bottom quarks
• Observation of $H\to b\bar{b}$ decays and $VH$ production with the ATLAS detector
• Power Counting to Better Jet Observables
• Seeing in Color: Jet Superstructure
• Sudakov Safety in Perturbative QCD
• New Angles on Energy Correlation Functions
• Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
• Performance of jet substructure techniques for large-R jets in proton-proton collisions at sqrt(s) = 7 TeV using the ATLAS detector
• The Lund Jet Plane
• Measurements of $WH$ and $ZH$ production in the $H\to b\bar{b}$ decay channel in $pp$ collisions at 13 TeV with the ATLAS detector
• Unsafe but Calculable: Ratios of Angularities in Perturbative QCD
• Boosting $H\to b\bar{b}$ with Machine Learning
• Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
• Studies of Boosted Decision Trees for MiniBooNE Particle Identification
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Measurement of the Lund jet plane using charged particles in 13 TeV proton-proton collisions with the ATLAS detector
• Measurement of the associated production of a Higgs boson decaying into $b$-quarks with a vector boson at high transverse momentum in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Benchmarking simplified template cross sections in $WH$ production
• Automating the Construction of Jet Observables with Machine Learning
• Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
• Measurement of $VH$, $H\to b\bar{b}$ production as a function of the vector-boson transverse momentum in 13 TeV $pp$ collisions with the ATLAS detector
• Measurement of colour flow with the jet pull angle in $t\bar{t}$ events using the ATLAS detector at $\sqrt{s}=8$ TeV
• Convolutional Neural Networks for Direct Detection of Dark Matter
• Higgs boson tagging with the Lund jet plane
• Calculating the primary Lund Jet Plane density
• Measurement of color flow in t-tbar events from p-pbar collisions at sqrt(s)=1.96 TeV
• Measurement of colour flow using jet-pull observables in $t\bar{t}$ events with the ATLAS experiment at $\sqrt{s}=13$ TeV
• An Optimal Observable for Color Singlet Identification
• Using Machine Learning to disentangle LHC signatures of Dark Matter candidates
• Theory Predictions for the Pull Angle
• Safe Use of Jet Pull
• Calculating Pull for Non-Singlet Jets