Enhanced Higgs Boson toτ+τ−Search with Deep Learning

2014/10/13 by Pierre Baldi, Peter Sadowski, Daniel Whiteson +1 · 104 citations
Computer Science · Physics and Astronomy · #Algorithm #Computer science #Cosmology and Gravitation Theories #Higgs boson #Particle physics #Particle physics theoretical and experimental studies #Physics #Quantum Chromodynamics and Particle Interactions #cs.LG #hep-ex #hep-ph

paper · pdf · doi:10.1103/physrevlett.114.111801

published in Physical Review Letters 114(11), 111801 (American Physical Society) · For submission to PRL

arxiv created 2014/10/13 · openalex publication_date 2015/03/18 · arxiv updated 2015/03/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/06

Abstract

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5σ significance barrier without more data. Deep learning techniques have the potential to increase the statistical power of this analysis by automatically learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs boson to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight nonlinear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated data set of 25%.

Citations

• Improving neural networks by preventing co-adaptation of feature detectors
• Searching for exotic particles in high-energy physics with deep learning

Cited by

• Invisible Higgs search through vector boson fusion: a deep learning approach
• Higgs self-coupling measurements using deep learning in the bbbb final state
• Jet flavor classification in high-energy physics with deep neural networks
• Vector boson scattering: Recent experimental and theory developments
• Polarization fraction measurement in same-sign WW scattering using deep learning
• Stacking machine learning classifiers to identify Higgs bosons at the LHC
• List of datasets for machine-learning research [wikipedia]

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