Artificial Intelligence for High Energy Physics

✍ Scribed by Paolo Calafiura (editor), David Rousseau (editor), Kazuhiro Terao (editor)

• Publisher: World Scientific
• Year: 2022
• Tongue: English
• Leaves: 829
• Category: Library

✦ Synopsis

The Higgs boson discovery at the Large Hadron Collider in 2012 relied on boosted decision trees. Since then, high energy physics (HEP) has applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, the availability of high-quality simulators, the complexity of the data structures (which rarely are image-like), the control of uncertainties expected from scientific measurements, and the exabyte-scale datasets require the development of HEP-specific ML techniques. While these developments proceed at full speed along many paths, the nineteen reviews in this book offer a self-contained, pedagogical introduction to ML models' real-life applications in HEP, written by some of the foremost experts in their area.

✦ Table of Contents

Contents
1. Introduction • Paolo Calafiura, David Rousseau and Kazuhiro Terao
Part I: Discriminative Models for Signal/Background Boosting
2. Boosted Decision Trees • Yann Coadou
3. Deep Learning from Four Vectors • Pierre Baldi, Peter Sadowski and Daniel Whiteson
4. Anomaly Detection for Physics Analysis and Less Than Supervised Learning • Benjamin Nachman
Part II: Data Quality Monitoring
5. Data Quality Monitoring Anomaly Detection • Adrian Alan Pol, Gianluca Cerminara, Cecile Germain and Maurizio Pierini
Part III: Generative Models
6. Generative Models for Fast Simulation • Michela Paganini, Luke de Oliveira, Benjamin Nachman, Denis Derkach, Fedor Ratnikov, Andrey Ustyuzhanin and Aishik Ghosh
7. Generative Networks for LHC Events • Anja Butter and Tilman Plehn
Part IV: Machine Learning Platforms
8. Distributed Training and Optimization of Neural Networks • Jean-Roch Vlimant and Junqi Yin
9. Machine Learning for Triggering and Data Acquisition • Philip Harris and Nhan Tran
Part V: Detector Data Reconstruction
10. End-to-End Analyses Using Image Classification • Adam Aurisano and Leigh H. Whitehead
11. Clustering • Kazuhiro Terao
12. Graph Neural Networks for Particle Tracking and Reconstruction • Javier Duarte and Jean-Roch Vlimant
Part VI: Jet Classification and Particle Identification from Low Level
13. Image-Based Jet Analysis • Michael Kagan
14. Particle Identification in Neutrino Detectors • Ralitsa Sharankova and Taritree Wongjirad
15. Sequence-Based Learning • Rafael Teixeira de Lima
Part VII: Physics Inference
16. Simulation-Based Inference Methods for Particle Physics • Johann Brehmer and Kyle Cranmer
17. Dealing with Nuisance Parameters • T. Dorigo and P. de Castro Manzano
18. Bayesian Neural Networks • Tom Charnock, Laurence Perreault-Levasseur and François Lanusse
19. Parton Distribution Functions • Stefano Forte and Stefano Carrazza
Part VIII: Scientific Competitions and Open Datasets
20. Machine Learning Scientific Competitions and Datasets • David Rousseau and Andrey Ustyuzhanin
Index