Machine and Deep Learning Applications in Particle Physics

Abstract

The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced, and cutting-edge applications in the experimental and theoretical/phenomenological domains are highlighted. After describing the challenges in the application of these novel analysis techniques, the review concludes by discussing the interactions between physics and machine learning as a two-way street enriching both disciplines and helping to meet the present and future challenges of data-intensive science at the energy and intensity frontiers.

arXiv comment29 pages, 2 figures, preprint of an article to be published in IJMPA

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• Uncertainty Aware Learning for High Energy Physics
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• A Cautionary Tale of Decorrelating Theory Uncertainties
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• Disentangling Boosted Higgs Boson Production Modes with Machine Learning
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• GPU coprocessors as a service for deep learning inference in high energy physics
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• Classifying near-threshold enhancement using deep neural network
• Quantum Vision Transformers for Quark-Gluon Classification
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• Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
• Realizing the potential of deep neural network for analyzing neutron star observables and dense matter equation of state
• Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach
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• Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning
• Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors
• Meta-learning and data augmentation for mass-generalised jet taggers
• Universes as Big Data
• Portable acceleration of CMS computing workflows with coprocessors as a service
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• Machine-Learning Performance on Higgs-Pair Production Associated with Dark Matter at the LHC
• Modeling early-universe energy injection with Dense Neural Networks
• Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects
• Influence of QCD parton shower in deep learning invisible Higgs through vector boson fusion
• Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field
• Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
• Renormalization-group-inspired neural networks for computing topological invariants
• Fast multilabel classification of HEP constraints with deep learning
• Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
• Neural networks for boosted di-$\tau$ identification
• Exploring the Universality of Hadronic Jet Classification
• Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy
• Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector
• Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks
• Distributed training and scalability for the particle clustering method UCluster
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
• Extraction of the color dipole amplitude with physics-informed neural networks
• Shedding Light on Dark Matter at the LHC with Machine Learning