| 書目名稱 | Search for Exotic Higgs Boson Decays to Merged Diphotons |
| 副標(biāo)題 | A Novel CMS Analysis |
| 編輯 | Michael Andrews |
| 視頻video | http://file.papertrans.cn/864/863017/863017.mp4 |
| 概述 | 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 domai |
| 叢書名稱 | Springer Theses |
| 圖書封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2023 |
| 關(guān)鍵詞 | Exotic Higgs Boson Decays; Merged Photons; End-to-end Deep Learning; End-to-end Physics Reconstruction; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-25091-0 |
| isbn_softcover | 978-3-031-25093-4 |
| isbn_ebook | 978-3-031-25091-0Series ISSN 2190-5053 Series E-ISSN 2190-5061 |
| issn_series | 2190-5053 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機版|小黑屋|
派博傳思國際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-8 02:59
發(fā)表于 2025-3-21 16:42:20
