Deep learning for the R-parity violating supersymmetry searches at the LHC

Abstract

Supersymmetry with hadronic R-parity violation in which the lightest neutralino decays into three quarks is still weakly constrained. This work aims to further improve the current search for this scenario by the boosted decision tree method with additional information from jet substructure. In particular, we find a deep neural network turns out to perform well in characterizing the neutralino jet substructure. We first construct a Convolutional Neutral Network (CNN) which is capable of tagging the neutralino jet in any signal process by using the idea of jet image. When applied to pure jet samples, such a CNN outperforms the N-subjettiness variable by a factor of a few in tagging efficiency. Moreover, we find the method, which combines the CNN output and jet invariant mass, can perform better and is applicable to a wider range of neutralino mass than the CNN alone. Finally, the ATLAS search for the signal of gluino pair production with subsequent decay $\tilde{g}\to qq{\tilde{\chi }}_1^0(\to qqq)$ is recasted as an application. In contrast to the pure sample, the heavy contamination among jets in this complex final state renders the discriminating powers of the CNN and N-subjettiness similar. By analyzing the jets substructure in events which pass the ATLAS cuts with our CNN method, the exclusion limit on gluino mass can be pushed up by $\sim 200$ GeV for neutralino mass $\sim 100$ GeV.

arXiv comment17 pages, 6 figures, version accepted by PRD

References in corpus (43)

• 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
• FastJet user manual
• Supersymmetric Dark Matter
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Jet substructure as a new Higgs search channel at the LHC
• Identifying Boosted Objects with N-subjettiness
• Matching matrix elements and shower evolution for top-quark production in hadronic collisions
• Jet Trimming
• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Towards Jetography
• Boosted objects: a probe of beyond the Standard Model physics
• Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks
• Classification without labels: Learning from mixed samples in high energy physics
• 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
• Parameterized Machine Learning for High-Energy Physics
• Boosted objects and jet substructure at the LHC
• Deep-learned Top Tagging with a Lorentz Layer
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• Search for squarks and gluinos in final states with jets and missing transverse momentum using 36 fb${}^{-1}$ of $\sqrt{s}$=13 TeV $pp$ collision data with the ATLAS detector
• What is the Machine Learning?
• Discovering baryon-number violating neutralino decays at the LHC
• How Much Information is in a Jet?
• Recursive Neural Networks in Quark/Gluon Tagging
• Novel Jet Observables from Machine Learning
• Hide and Seek With Natural Supersymmetry at the LHC
• Search for natural and split supersymmetry in proton-proton collisions at $\sqrt{s}=$ 13 TeV in final states with jets and missing transverse momentum
• (Machine) Learning to Do More with Less
• Search for massive supersymmetric particles decaying to many jets using the ATLAS detector in $pp$ collisions at $\sqrt{s}=8$ TeV
• Natural SUSY's Last Hope: R-parity Violation via UDD Operators
• Cornering Natural SUSY at LHC Run II and Beyond
• Pulling Out All the Stops: Searching for RPV SUSY with Stop-Jets
• High Multiplicity Searches at the LHC Using Jet Masses
• Learning How to Count: A High Multiplicity Search for the LHC
• The same-sign top signature of R-parity violation
• Study of Baryonic R-Parity Violating MSSM Using Jet Substructure Technique at the 14 TeV LHC
• Jet Substructure Templates: Data-driven QCD Backgrounds for Fat Jet Searches
• Stashing the stops in multijet events at the LHC

Cited by in corpus (18)

• Searching for New Physics with Deep Autoencoders
• QCD or What?
• A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
• Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
• Topological Obstructions to Autoencoding
• Electromagnetic Showers Beyond Shower Shapes
• Reconstructing boosted Higgs jets from event image segmentation
• Invisible Higgs search through Vector Boson Fusion: A deep learning approach
• MLAnalysis: An open-source program for high energy physics analyses
• Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC
• Electroweakino searches at the HL-LHC in the baryon number violating MSSM
• Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar{t}$ resonance at the LHC
• Probing Higgs exotic decay at the LHC with machine learning
• Improving the measurement of the Higgs boson-gluon coupling using convolutional neural networks at $e^{+}{e}^{-}$ colliders
• Fast multilabel classification of HEP constraints with deep learning
• Signatures of Supersymmetry in Neutrino Telescopes
• Solving Combinatorial Problems at Particle Colliders Using Machine Learning
• Top squark signal significance enhancement by different Machine Learning Algorithms