Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network

Abstract

Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machine-learning techniques have potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.

32 pages, 17 figures; version accepted for publication in JHEP

References in corpus (49)

• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• High-Energy-Physics Event Generation with PYTHIA 6.1
• Theories with Gauge-Mediated Supersymmetry Breaking
• R-parity violating supersymmetry
• Supersymmetric Unification Without Low Energy Supersymmetry And Signatures for Fine-Tuning at the LHC
• Echoes of a Hidden Valley at Hadron Colliders
• Description and performance of track and primary-vertex reconstruction with the CMS tracker
• Illuminating Dark Photons with High-Energy Colliders
• Mini-Split
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Energy Flow Networks: Deep Sets for Particle Jets
• Extending the Bump Hunt with Machine Learning
• A Heavy Gluino as the Lightest Supersymmetric Particle
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• How to GAN LHC Events
• QCD or What?
• A generic anti-QCD jet tagger
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Signatures of long-lived gluinos in split supersymmetry
• Detecting Gluino-Containing Hadrons
• Interactions of Heavy Stable Hadronizing Particles
• A Stealth Supersymmetry Sampler
• Boosting $H\to b\bar{b}$ with Machine Learning
• Probing Baryogenesis with Displaced Vertices at the LHC
• Displaced vertex signatures of doubly charged scalars in the type-II seesaw and its left-right extensions
• Long-lived particles at the LHC: catching them in time
• Stopping Gluinos
• Long-Lived Neutralino NLSPs
• Long Lived Light Scalars as Probe of Low Scale Seesaw Models
• Searches for light sterile neutrinos with multitrack displaced vertices
• Pure Gravity Mediation of Supersymmetry Breaking at the LHC
• Triggers for displaced decays of long-lived neutral particles in the ATLAS detector
• Probing stop pair production at the LHC with graph neural networks
• An analysis of a Heavy Gluino LSP at CDF : The Heavy Gluino Window
• Gauge-Mediated Supersymmetry Breaking: Introduction, Review and Update
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Portraying Double Higgs at the Large Hadron Collider
• Long Live the Higgs Factory: Higgs Decays to Long-Lived Particles at Future Lepton Colliders
• Signatures of sneutrino dark matter in an extension of the CMSSM
• Long-lived stau, sneutrino dark matter and right-slepton spectrum
• Cornering Compressed Gluino at the LHC
• Novel signatures for long-lived particles at the LHC
• Searching for Metastable Particles with Sub-Millimeter Displaced Vertices at Hadron Colliders
• Scope of strongly self-interacting thermal WIMPs in a minimal $U(1{)}_D$ extension and its future prospects
• Non-thermal WIMP baryogenesis
• A simulation study to distinguish prompt photon from ${\pi }^0$ and beam halo in a granular calorimeter using deep networks

Cited by in corpus (13)

• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Boosted $W/Z$ Tagging with Jet Charge and Deep Learning
• Enhancing Sensitivities to Long-lived Particles with High Granularity Calorimeters at the LHC
• Probing triple Higgs coupling with machine learning at the LHC
• Long-Lived Light Mediators from Higgs boson Decay at HL-LHC, FCC-hh and a Proposal of Dedicated LLP Detectors for FCC-hh
• Determining the lifetime of long-lived particles at the HL-LHC
• LHC signals of triplet scalars as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks
• Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter
• Dedicated Triggers for Displaced Jets using Timing Information from Electromagnetic Calorimeter at HL-LHC
• Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliders
• Multi-Higgs Boson Probes of the Dark Sector
• Top-philic Machine Learning
• Long-lived Light Mediators in a Higgs Portal Model at the FCC-ee