A Living Review of Machine Learning for Particle Physics

Abstract

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.

arXiv comment3 pages, 3 figures, GitHub repository of Living Review https://github.com/iml-wg/HEPML-LivingReview

Cited by in corpus (37)

• A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
• Mining for Gluon Saturation at Colliders
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• ABCDisCo: Automating the ABCD Method with Machine Learning
• Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
• Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Presenting Unbinned Differential Cross Section Results
• Les Houches 2021: Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
• The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
• E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once
• Inference of cosmic-ray source properties by conditional invertible neural networks
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
• Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
• Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning
• SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning
• A high-granularity calorimeter insert based on SiPM-on-tile technology at the future Electron-Ion Collider
• Non-Parametric Data-Driven Background Modelling using Conditional Probabilities
• Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
• Towards a method to anticipate dark matter signals with deep learning at the LHC
• High-dimensional Anomaly Detection with Radiative Return in $e^{+}{e}^{-}$ Collisions
• Les Houches 2023 -- Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
• Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis
• Set-Conditional Set Generation for Particle Physics
• Partition Pooling for Convolutional Graph Network Applications in Particle Physics
• Distributed Training and Optimization Of Neural Networks
• Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability
• Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
• Deep-Learned Event Variables for Collider Phenomenology
• PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
• Segmentation of EM showers for neutrino experiments with deep graph neural networks
• Identifying the Quantum Properties of Hadronic Resonances using Machine Learning
• Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• How to use Machine Learning to improve the discrimination between signal and background at particle colliders
• Lecture notes on Machine Learning applications for global fits