Higgs boson tagging with the Lund jet plane

Abstract

We construct a procedure to separate boosted Higgs bosons decaying into hadrons, from the background due to strong interactions. We employ the Lund jet plane to obtain a theoretically well-motivated representation of the jets of interest and we use the resulting images as the input to a convolutional neural network classifier. In particular, we consider two different decay modes of the Higgs boson, namely into a pair of bottom quarks or into light jets, against the respective backgrounds. For each case, we consider both a moderate- and high- boost scenario. The performance of the tagger is compared to what is achieved using a traditional single-variable analysis which exploits a QCD inspired color-singlet tagger, namely the jet color ring observable.

arXiv comment10 pages, 7 figures, published version

References in corpus (50)

• PYTHIA 6.4 Physics and Manual
• PYTHIA 6.2 Physics and Manual
• 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
• A Brief Introduction to PYTHIA 8.1
• FastJet user manual
• Better Jet Clustering Algorithms
• Towards an understanding of jet substructure
• Deep Learning and its Application to LHC Physics
• Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Energy Flow Networks: Deep Sets for Particle Jets
• The Machine Learning Landscape of Top Taggers
• Constraining Effective Field Theories with Machine Learning
• A Guide to Constraining Effective Field Theories with Machine Learning
• Energy flow polynomials: A complete linear basis for jet substructure
• Pulling Out All the Tops with Computer Vision and Deep Learning
• The Lund Jet Plane
• Jet substructure with analytical methods
• Boosting $H\to b\bar{b}$ with Machine Learning
• How Much Information is in a Jet?
• Novel Jet Observables from Machine Learning
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• Jet Charge and Machine Learning
• Measurement of the Lund jet plane using charged particles in 13 TeV proton-proton collisions with the ATLAS detector
• Quark-Gluon Tagging: Machine Learning vs Detector
• On jet substructure methods for signal jets
• Learning the latent structure of collider events
• Lund jet images from generative and cycle-consistent adversarial networks
• Exploring SMEFT in VH with Machine Learning
• A Theory of Quark vs. Gluon Discrimination
• Mass Unspecific Supervised Tagging (MUST) for boosted jets
• The Boosted Higgs Jet Reconstruction via Graph Neural Network
• Automating the Construction of Jet Observables with Machine Learning
• Towards Machine Learning Analytics for Jet Substructure
• Convolutional Neural Networks for Direct Detection of Dark Matter
• Parametrized classifiers for optimal EFT sensitivity
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• Calculating the primary Lund Jet Plane density
• The DNNLikelihood: enhancing likelihood distribution with Deep Learning
• Higgs self-coupling measurements using deep learning in the $b\bar{b}b\bar{b}$ final state
• An Optimal Observable for Color Singlet Identification
• Invisible Higgs search through Vector Boson Fusion: A deep learning approach
• Reconstructing boosted Higgs jets from event image segmentation
• Using Machine Learning to disentangle LHC signatures of Dark Matter candidates
• Jet tagging made easy
• Towards recognizing the light facet of the Higgs Boson
• Sensing Higgs cascade decays through memory

Cited by in corpus (17)

• Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm
• Measuring the electron Yukawa coupling via resonant s-channel Higgs production at FCC-ee
• Discovery prospects of a vectorlike top partner decaying to a singlet boson
• Leveraging universality of jet taggers through transfer learning
• Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC
• Identification of b-jets using QCD-inspired observables
• Equivariant, Safe and Sensitive -- Graph Networks for New Physics
• BIP: Boost Invariant Polynomials for Efficient Jet Tagging
• Is infrared-collinear safe information all you need for jet classification?
• Dark Sector Showers in the Lund Jet Plane
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Heavy Flavour Jet Substructure
• Riemannian Data preprocessing in Machine Learning to focus on QCD color structure
• HL-LHC sensitivity to higgsinos from natural SUSY with gravitino LSP
• Tagging the Higgs boson decay to bottom quarks with colour-sensitive observables and the Lund jet plane
• Jet substructure of light and heavy flavor jets at RHIC
• Graph theory inspired anomaly detection at the LHC