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Record Nr. |
UNINA9910678250403321 |
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Autore |
Andrews Michael <1835-1917, > |
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Titolo |
Search for exotic Higgs boson decays to merged diphotons : a novel CMS analysis using end-to-end deep learning / / Michael Andrews |
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Pubbl/distr/stampa |
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Berlin, Germany : , : Springer, , [2023] |
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©2023 |
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ISBN |
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9783031250910 |
9783031250903 |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (193 pages) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5061 |
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Disciplina |
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Soggetti |
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Deep learning (Machine learning) |
Higgs bosons |
Particles (Nuclear physics) - Diffraction |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- The LHC and the CMS detector -- Theory & phenomenology -- Analysis strategy -- Data sets -- Signal selection -- a mass regression -- Analysis -- Results -- Conclusions -- Supplementary studies. |
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Sommario/riassunto |
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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 |
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