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.

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

Cited by in corpus (17)

• A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic
• Mining for Gluon Saturation at Colliders
• Les Houches 2021: Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
• Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
• 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
• 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
• Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
• PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
• Deep-Learned Event Variables for Collider Phenomenology
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• Lecture notes on Machine Learning applications for global fits