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出版商:
Springer
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出版日期:
2026-01-03
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售價:
$7,340
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貴賓價:
9.5 折
$6,973
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語言:
英文
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頁數:
350
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裝訂:
Hardcover - also called cloth, retail trade, or trade
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ISBN:
3031962877
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ISBN-13:
9783031962875
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相關分類:
Machine Learning
商品描述
This book reports the successful optimization of the Compact Mupn Solenoid (CMS) tau trigger algorithm for the Run-3 (Phase-1) of the Large Hadron Collider (LHC) and a completely new and original design of a machine learning based tau triggering algorithm for the High Luminosity LHC (or Phase-2). A large proportion of searches at collider experiments relies on datasets collected with a dedicated tau lepton selection algorithm, particularly difficult to operate in intense hadronic environments, making the work descirbed in this book of prime importance. The second part of the book describes a major and very challenging data analysis, aiming to detect Higgs boson pair production. The book summarizes these contributions in clear, pedagogical prose while keeping an adequate and coherent balance between the technical and data analysis aspects. Machine learning techniques were used extensively throughout this research; therefore, special care has been taken to describe their core principles and application in high-energy physics, as well as potential future developments for sophisticated low-latency trigger algorithms and modern signal extraction methods.
商品描述(中文翻譯)
本書報告了在大型強子對撞機（LHC）第三階段（Phase-1）中，成功優化了緊湊型 Mupn 電磁閥（CMS）tau 觸發演算法，以及針對高亮度 LHC（或 Phase-2）全新且原創的基於機器學習的 tau 觸發演算法。許多對撞機實驗的搜尋工作依賴於專門的 tau 輕子選擇演算法所收集的數據集，這在強烈的強子環境中尤其困難，因此本書中所描述的工作具有重要意義。本書的第二部分描述了一項主要且非常具挑戰性的數據分析，旨在檢測希格斯玻色子對的產生。書中以清晰且具教學性的文筆總結了這些貢獻，同時在技術與數據分析方面保持適當且一致的平衡。本研究廣泛使用了機器學習技術，因此特別注意描述其核心原則及在高能物理中的應用，以及對於複雜低延遲觸發演算法和現代信號提取方法的潛在未來發展。
作者簡介
Jona Motta is a particle physicist from Italy, born in 1996. He obtained his B.Sc. degree in Physics at the University of Milano Bicocca, with a dissertation entitled "Performance studies for Higgs pair searches at LHC with the CMS detector" under the supervision of Dr. Pietro Govoni. He obtained a Joint M.Sc. degree in High Energy Physics at ETH Zürich and École Polytechnique Paris, with two dissertations titled "Testing Lepton Flavour Universality in semi-leptonic decays of the Bc+ meson: a feasibility study in CMS" under the supervision of Prof. Dr. Günther Dissertori, and "Study of the Higgs boson self-coupling in the bbττ decay channel" under the supervision of Dr. Roberto Salerno. During his studies, Jona joined the CMS Collaboration in 2020. Jona worked on his Ph.D. thesis at the Laboratoire Leprince Ringuet (LLR) at the École Polytechnique in Paris, working on the development of a completely new and original design of a machine learning based τ triggering algorithm for CMS at the High Luminosity LHC (or Phase-2), and searching for Higgs boson pair production in the bbττ final state. He is currently a postdoctoral researcher at the University of Zürich, and his main research interests are the search for Higgs boson pair production and the searches for additional bosons that could reveal the presence of physics beyond the Standard Model. Alongside these physics interests, Jona continues to develop machine learning techniques that aim at boosting the sensitivy of physics analyses at CMS.
作者簡介(中文翻譯)
喬納·莫塔（Jona Motta）是一位來自義大利的粒子物理學家，出生於1996年。他在米蘭比科卡大學（University of Milano Bicocca）獲得物理學學士學位，論文題目為「使用CMS探測器在LHC上進行希格斯對的搜尋性能研究」，指導教授為皮耶特羅·戈沃尼博士（Dr. Pietro Govoni）。他在蘇黎世聯邦理工學院（ETH Zürich）和巴黎高等師範學院（École Polytechnique Paris）獲得高能物理的聯合碩士學位，兩篇論文分別為「在Bc+介子半輻射衰變中測試輕子味的普遍性：CMS中的可行性研究」，指導教授為古恩特·迪瑟托里教授（Prof. Dr. Günther Dissertori），以及「在bbττ衰變通道中研究希格斯玻色子的自耦合」，指導教授為羅伯托·薩萊諾博士（Dr. Roberto Salerno）。在學期間，喬納於2020年加入CMS合作組織。喬納在巴黎的勒普林斯·林蓋實驗室（Laboratoire Leprince Ringuet, LLR）進行博士論文研究，專注於為高亮度LHC（或第二階段）開發一種全新且原創的基於機器學習的τ觸發算法，並搜尋bbττ最終態中的希格斯玻色子對產生。目前，他是蘇黎世大學的博士後研究員，主要研究興趣為希格斯玻色子對的產生搜尋以及尋找可能揭示超越標準模型物理的額外玻色子。除了這些物理研究興趣外，喬納還持續開發機器學習技術，旨在提升CMS物理分析的靈敏度。
