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211103 ||| eng |
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|a 9783030879389
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| 100 |
1 |
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|a Tosciri, Cecilia
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| 245 |
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|a Higgs Boson Decays into a Pair of Bottom Quarks
|h Elektronische Ressource
|b Observation with the ATLAS Detector and Machine Learning Applications
|c by Cecilia Tosciri
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| 250 |
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|a 1st ed. 2021
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2021, 2021
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| 300 |
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|a XIX, 159 p. 84 illus., 76 illus. in color
|b online resource
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| 505 |
0 |
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|a Introduction -- Theoretical Introduction -- Machine Learning -- The LHC and the ATLAS Detector -- Physics Object Reconstruction
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| 653 |
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|a Machine learning
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| 653 |
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|a Quantum field theory
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| 653 |
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|a Machine Learning
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| 653 |
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|a Elementary particles (Physics)
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| 653 |
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|a Elementary Particles, Quantum Field Theory
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| 653 |
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|a Particles (Nuclear physics)
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| 653 |
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|a Particle Physics
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Springer Theses, Recognizing Outstanding Ph.D. Research
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| 028 |
5 |
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|a 10.1007/978-3-030-87938-9
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-030-87938-9?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 530.14
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| 520 |
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|a This thesis presents the analysis that led to the observation of the Standard Model (SM) Higgs boson decay into pairs of bottom quarks. The analysis, based on a multivariate strategy, exploits the production of a Higgs boson associated with a vector boson. The analysis was performed on a dataset corresponding to a luminosity of 79.8/fb collected by the ATLAS experiment during Run-2 at a centre-of-mass energy of 13 TeV. An excess of events over the expected background is observed in a combination with complementary Hbb searches. The analysis was extended to provide a finer interpretation of the signal measurement. The cross sections of the V H(H → bb) process have been measured in exclusive regions of phase space and used to search for deviations from the SM with an effective field theory approach. The results are discussed in this book. A novel technique for the fast simulation of the ATLAS forward calorimeterresponse is also presented. The new technique is based on similarity search, a branch of machine learning that enables quick and efficient searches for vectors similar to each other
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