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Search for tt H Production in the H bb Decay Channel - Using Deep Learning Techniques with the CMS Experiment (Hardcover, 1st ed. 2021) Loot Price: R2,886
Discovery Miles 28 860
Search for tt H Production in the H   bb  Decay Channel - Using Deep Learning Techniques with the CMS Experiment (Hardcover,...

Search for tt H Production in the H bb Decay Channel - Using Deep Learning Techniques with the CMS Experiment (Hardcover, 1st ed. 2021)

Marcel Rieger

Series: Springer Theses

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Loot Price R2,886 Discovery Miles 28 860 | Repayment Terms: R270 pm x 12*

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In 1964, a mechanism explaining the origin of particle masses was proposed by Robert Brout, Francois Englert, and Peter W. Higgs. 48 years later, in 2012, the so-called Higgs boson was discovered in proton-proton collisions recorded by experiments at the LHC. Since then, its ability to interact with quarks remained experimentally unconfirmed. This book presents a search for Higgs bosons produced in association with top quarks tt H in data recorded with the CMS detector in 2016. It focuses on Higgs boson decays into bottom quarks H bb and top quark pair decays involving at least one lepton. In this analysis, a multiclass classification approach using deep learning techniques was applied for the first time. In light of the dominant background contribution from tt production, the developed method proved to achieve superior sensitivity with respect to existing techniques. In combination with searches in different decay channels, the presented work contributed to the first observations of tt H production and H bb decays.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Series: Springer Theses
Release date: February 2021
First published: 2021
Authors: Marcel Rieger
Dimensions: 235 x 155mm (L x W)
Format: Hardcover
Pages: 217
Edition: 1st ed. 2021
ISBN-13: 978-3-03-065379-8
Categories: Books > Science & Mathematics > Mathematics > Probability & statistics
Books > Science & Mathematics > Physics > Particle & high-energy physics
Books > Science & Mathematics > Physics > Quantum physics (quantum mechanics) > General
Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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LSN: 3-03-065379-X
Barcode: 9783030653798

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