Modern Machine Learning for LHC Physicists

Abstract

Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC physics tasks. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, data representations, and inverse problems. Three themes defining much of the discussion are statistically defined loss functions, uncertainties, and accuracy. To understand the applications, the notes include some aspects of theoretical LHC physics. All examples are chosen from particle physics publications of the last few years, and many of them come with corresponding tutorials.

Further expanded on uncertainties, representation learning, unfolding, etc

Cited by in corpus (26)

• MadNIS -- Neural Multi-Channel Importance Sampling
• Two Invertible Networks for the Matrix Element Method
• The MadNIS Reloaded
• Returning CP-Observables to The Frames They Belong
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• The Landscape of Unfolding with Machine Learning
• Precision-Machine Learning for the Matrix Element Method
• Anomalies, Representations, and Self-Supervision
• A Lorentz-Equivariant Transformer for All of the LHC
• Normalizing Flows for High-Dimensional Detector Simulations
• Differentiable MadNIS-Lite
• Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN
• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• Advancing Tools for Simulation-Based Inference
• SKATR: A Self-Supervised Summary Transformer for SKA
• Refinable modeling for unbinned SMEFT analyses
• Generative Unfolding with Distribution Mapping
• Amplitude Uncertainties Everywhere All at Once
• Extrapolating Jet Radiation with Autoregressive Transformers
• BitHEP -- The Limits of Low-Precision ML in HEP
• How to Unfold Top Decays
• Amplitude Surrogates for Multi-Jet Processes
• Systematically Constructing the Likelihood for Boosted $H\to gg$ Decays
• Direct reconstruction of the Reionization history from 21cm 2D Power Spectra
• Large Language Models -- the Future of Fundamental Physics?
• The Physics Behind ML-based Quark-Gluon Taggers