Overview
- Describes first application at CMS of deep learning directly on low-level, "raw" detector data
- Reports on first direct search for exotic Higgs boson decays involving boosted particle decays
- Uses domain continuation technique to reconstruct particle decays with invariant mass below detector resolution
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (10 chapters)
Keywords
About this book
This book describes the first application at CMS of deep learning algorithms trained directly on low-level, “raw” detector data, or so-called end-to-end physics reconstruction. Growing interest in searches for exotic new physics in the CMS collaboration at the Large Hadron Collider at CERN has highlighted the need for a new generation of particle reconstruction algorithms. For many exotic physics searches, sensitivity is constrained not by the ability to extract information from particle-level data but by inefficiencies in the reconstruction of the particle-level quantities themselves. The technique achieves a breakthrough in the reconstruction of highly merged photon pairs that are completely unresolved in the CMS detector. This newfound ability is used to perform the first direct search for exotic Higgs boson decays to a pair of hypothetical light scalar particles H→aa, each subsequently decaying to a pair of highly merged photons a→yy, an analysis once thought impossible to perform. The book concludes with an outlook on potential new exotic searches made accessible by this new reconstruction paradigm.
Authors and Affiliations
About the author
Michael Andrews completed his Ph.D. in Physics at Carnegie Mellon University where he was involved with the CMS collaboration at the Large Hadron Collider at CERN. He worked at CERN in Geneva, Switzerland, from 2015 to 2019 where he served as Run Coordinator for the CMS electromagnetic calorimeter group. For his distinguished service to CMS detector operations, he received the CMS Achievement Award in 2018.
Michael’s physics research focuses on the application advanced deep learning techniques to problems in LHC physics. He played a leading role in the development of deep learning algorithms trained directly on low-level detector data, so-called end-to-end physics reconstruction. His work on end-to-end physics reconstruction led to the first CMS results demonstrating the breakthrough potential of this technique over traditional methods for the reconstruction of boosted decays to highly merged photons. For his contributions, summarized in his Ph.D. thesis, he was awardedthe CMS Ph.D. Thesis Award in 2021.Bibliographic Information
Book Title: Search for Exotic Higgs Boson Decays to Merged Diphotons
Book Subtitle: A Novel CMS Analysis Using End-to-End Deep Learning
Authors: Michael Andrews
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-031-25091-0
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-25090-3Published: 03 March 2023
Softcover ISBN: 978-3-031-25093-4Published: 03 March 2024
eBook ISBN: 978-3-031-25091-0Published: 02 March 2023
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XIII, 188
Number of Illustrations: 10 b/w illustrations, 77 illustrations in colour
Topics: Particle and Nuclear Physics, Artificial Intelligence, Elementary Particles, Quantum Field Theory