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OverviewThe 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. Full Product DetailsAuthor: Paolo Calafiura (Lawrence Berkeley Nat'l Lab, Usa) , David Rousseau (Laboratoire De Physique Des 2 Infinis Irene Joliot-curie, France) , Kazuhiro Terao (Slac National Accelerator Lab, Usa)Publisher: World Scientific Publishing Co Pte Ltd Imprint: World Scientific Publishing Co Pte Ltd ISBN: 9789811234026ISBN 10: 9811234027 Pages: 828 Publication Date: 14 March 2022 Audience: Professional and scholarly , College/higher education , Professional & Vocational , Tertiary & Higher Education Format: Hardback Publisher's Status: Active Availability: Out of stock ![]() The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |