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oai:dspace.mit.edu:1721.1/1575782024-11-19T03:02:18Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Harris, Philip Coleman Krupa, Jeffrey Massachusetts Institute of Technology. Department of Physics 2024-11-18T19:12:05Z 2024-11-18T19:12:05Z 2024-09 2024-10-24T16:13:35.428Z https://hdl.handle.net/1721.1/157578 In this thesis, we introduce machine learning (ML) tools to optimize data taking and analysis at data-intensive scientific experiments, focusing on the CMS experiment at the Large Hadron Collider (LHC). A path to a foundation model for LHC physics is described, where self-supervised learning is enabled through the re-simulation of decaying partons. The first experiments with remote operation of GPUs in LHC experiments are presented. These tools will help equip experiments at the High-Luminosity LHC (HL-LHC) to perform precision measurements and searches for new physics, for example, low mass resonances decaying to quarks. In this context, a search for narrow resonances decaying into quarkantiquark pairs produced with high transverse momentum is presented. The analysis is based on data collected in Run 2 with the CMS detector at the LHC in proton-proton collisions at √ 𝑠 = 13 TeV. Resonance candidates are reconstructed as large-radius jets and identified using a state-of-the-art jet tagging algorithm. This analysis presents the most sensitive limits for new spin-1 bosons coupling universally to quarks and spin-0 bosons coupling preferentially to heavier quarks. The invariant jet mass spectrum is probed for a potential narrow peaking signal over a smoothly falling background. Upper limits at 95% confidence level are set on the coupling of narrow resonances to quarks as a function of the resonance mass. For masses between 50 and 300 GeV, these are the most sensitive limits to date on all possible mediators. Using conventions on s-channel dark matter mediators, limits are set on dark photons and dark matter in the context of the relic density. Ph.D. Massachusetts Institute of Technology Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ Exploring New Frontiers in High Energy Physics: Boosted Resonances Decaying To Quarks, Foundation Models, and Heterogeneous Computing at the CMS Experiment Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f147861b-d93c-4bfd-be8c-21ae4df7aff3"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Exploring New Frontiers in High Energy Physics: Boosted Resonances Decaying To Quarks, Foundation Models, and Heterogeneous Computing at the CMS Experiment</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2024-09</PublicationDate> <Authors> <Author> <DisplayName>Krupa, Jeffrey</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/licenses/by-nc-nd/4.0/</License> <Abstract>In this thesis, we introduce machine learning (ML) tools to optimize data taking and analysis at data-intensive scientific experiments, focusing on the CMS experiment at the Large Hadron Collider (LHC). A path to a foundation model for LHC physics is described, where self-supervised learning is enabled through the re-simulation of decaying partons. The first experiments with remote operation of GPUs in LHC experiments are presented. These tools will help equip experiments at the High-Luminosity LHC (HL-LHC) to perform precision measurements and searches for new physics, for example, low mass resonances decaying to quarks. In this context, a search for narrow resonances decaying into quarkantiquark pairs produced with high transverse momentum is presented. The analysis is based on data collected in Run 2 with the CMS detector at the LHC in proton-proton collisions at √ 𝑠 = 13 TeV. Resonance candidates are reconstructed as large-radius jets and identified using a state-of-the-art jet tagging algorithm. This analysis presents the most sensitive limits for new spin-1 bosons coupling universally to quarks and spin-0 bosons coupling preferentially to heavier quarks. The invariant jet mass spectrum is probed for a potential narrow peaking signal over a smoothly falling background. Upper limits at 95% confidence level are set on the coupling of narrow resonances to quarks as a function of the resonance mass. For masses between 50 and 300 GeV, these are the most sensitive limits to date on all possible mediators. Using conventions on s-channel dark matter mediators, limits are set on dark photons and dark matter in the context of the relic density.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>