Search for Exotic Higgs Boson Decays to Merged Diphotons: A Novel CMS Analysis Using End-To-End Deep Learning (尋找異常希格斯玻色子衰變至合併雙光子：一項使用端到端深度學習的新型CMS分析)

Andrews, Michael

Search for Exotic Higgs Boson Decays to Merged Diphotons: A Novel CMS Analysis Using End-To-End Deep Learning
  • 出版商: Springer
  • 出版日期: 2024-03-03
  • 售價: $6,460
  • 貴賓價: 9.5 折 $6,137
  • 語言: 英文
  • 頁數: 188
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031250931
  • ISBN-13: 9783031250934
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

本書描述了在CMS首次應用深度學習算法，這些算法直接在低層次的「原始」探測器數據上進行訓練，或稱為端到端的物理重建。隨著在CERN的大型強子對撞機上，CMS合作組對於尋找新型異常物理的興趣日益增長，突顯了對新一代粒子重建算法的需求。對於許多異常物理的搜尋，靈敏度的限制並不在於從粒子級數據中提取信息的能力，而在於粒子級量測本身重建的低效率。這項技術在重建在CMS探測器中完全無法解析的高度合併光子對方面取得了突破。這一新能力被用來首次直接搜尋異常希格斯玻色子衰變為一對假設的輕量標量粒子H→aa，每個粒子隨後衰變為一對高度合併的光子a→yy，這項分析曾被認為是不可能執行的。本書最後展望了這一新重建範式所能開啟的潛在新異常搜尋。

作者簡介

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.

作者簡介(中文翻譯)

邁克爾·安德魯斯（Michael Andrews）在卡內基梅隆大學（Carnegie Mellon University）完成了他的物理學博士學位，期間參與了位於歐洲核子研究組織（CERN）的大型強子對撞機（Large Hadron Collider）CMS合作計畫。他於2015年至2019年間在瑞士日內瓦的CERN工作，擔任CMS電磁量測儀小組的運行協調員。因其對CMS探測器操作的卓越貢獻，他於2018年獲得CMS成就獎。

邁克爾的物理研究專注於將先進的深度學習技術應用於LHC物理問題。他在直接基於低層探測器數據訓練的深度學習演算法的開發中扮演了重要角色，這被稱為端到端物理重建（end-to-end physics reconstruction）。他在端到端物理重建方面的工作，首次展示了這一技術在重建增強衰變至高度合併光子的傳統方法中的突破潛力。因其貢獻，總結於他的博士論文中，他於2021年獲得CMS博士論文獎。

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