Disentangling Boosted Higgs Boson Production Modes with Machine Learning

Abstract

Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson, and production of a Higgs boson with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.

17 pages, 9 figures

References in corpus (14)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• ADADELTA: An Adaptive Learning Rate Method
• The Catchment Area of Jets
• The PDF4LHC Working Group Interim Recommendations
• New Angles on Energy Correlation Functions
• Higgs characterisation at NLO in QCD: CP properties of the top-quark Yukawa interaction
• Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks
• Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• RIP $Hb\bar{b}$: How other Higgs production modes conspire to kill a rare signal at the LHC
• Precise predictions for boosted Higgs production
• Invisible Higgs search through Vector Boson Fusion: A deep learning approach
• Reconstructing boosted Higgs jets from event image segmentation
• TASI Lectures on Jet Substructure
• An Attention Based Neural Network for Jet Tagging

Cited by in corpus (9)

• Learning to Identify Semi-Visible Jets
• Probing a ${\mathrm{Z}}^{\prime }$ with non-universal fermion couplings through top quark fusion, decays to bottom quarks, and machine learning techniques
• Deeply Learned Preselection of Higgs Dijet Decays at Future Lepton Colliders
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
• Probing Compressed Mass Spectrum Supersymmetry at the LHC with the Vector Boson Fusion Topology
• Probing Light Scalars and Vector-like Quarks at the High-Luminosity LHC
• HL-LHC sensitivity to higgsinos from natural SUSY with gravitino LSP
• Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC