Artificial Intelligence For High Energy Physics

The Higgs boson discovery in 2012 led to the application of modern machine learning techniques in high energy physics (HEP) data analysis. This requires the development of HEP-specific ML techniques due to the unique requirements of HEP data, such as the control of uncertainties, the complexity of data structures, and the exabyte-scale datasets. This book offers nineteen reviews that provide a self-contained introduction to ML models and their real-life applications in HEP, written by experts in the field.

Format: Hardback
Length: 828 pages
Publication date: 13 March 2022
Publisher: World Scientific Publishing Co Pte Ltd

The groundbreaking discovery of the Higgs boson at the Large Hadron Collider in 2012 marked a significant milestone in high energy physics (HEP). Since then, HEP has embraced modern machine learning (ML) techniques across all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, including the handling of vast datasets, complex data structures, control of uncertainties, and the need for specialized techniques, have driven the development of HEP-specific ML approaches. While these advancements continue to flourish along various paths, this book presents nineteen comprehensive reviews authored by esteemed experts in the field. These reviews offer a self-contained and pedagogical introduction to ML models, showcasing their real-life applications in HEP. By delving into the intricacies of these models, readers will gain a deeper understanding of the potential of ML to revolutionize the field of HEP and unlock new insights into the fundamental nature of the universe.

ISBN-13: 9789811234026