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Searches for Nonresonant Higgs Boson Pair Production and Long-Lived Particles at the LHC and Machine-Learning Solutions for the High-Luminosity LHC Era


Nguyen, Thong Quang (2022) Searches for Nonresonant Higgs Boson Pair Production and Long-Lived Particles at the LHC and Machine-Learning Solutions for the High-Luminosity LHC Era. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/knfz-q495.


This thesis presents two physics analyses using 137 fb−1 proton-proton collision data collected by the CMS experiment at √s = 13 TeV, along with a series of machine-learning solutions to extend the physics program at the LHC and to address the computational challenges in the High-Luminosity LHC era. The first analysis searches for nonresonant Higgs boson pair production in final states with two photons and two bottom quarks, with no significant deviation from the background-only hypothesis observed. The observed (expected) upper limit on the product of the Higgs boson pair production cross section and branching fraction into bb̅γγ is 0.67 (0.45) fb, corresponding to 7.7 (5.2) times the Standard Model prediction. The modifier of the Higgs trilinear self-coupling is constrained within the range -3.3 < κλ < 8.5. The modifier for coupling between a pair of Higgs bosons and a pair of vector bosons, along with the 2-dimensional constraint of the modifiers of Higgs self-coupling and Yukawa coupling, are also reported. A graph-based algorithm to identify boosted H → bb̅ jets to improve future Higgs search is presented. The second analysis searches for long-lived supersymmetry particles decaying to photons and gravitinos in the context of gauge-mediated supersymmetry breaking model. Results are presented in terms of 95% confidence level expected exclusion limits on the masses and proper decay lengths of the neutralino, which exceed the limits from the previous searches by up to 100 GeV for the neutralino mass and by five times for the neutralino proper decay length. A strategy for model-independent new physics searches is presented with an anomaly trigger based on unsupervised learning algorithms that can be deployed in both the high-level trigger and the Level-1 trigger in CMS. Three other machine-learning solutions are presented to address the computational challenges in the HL-LHC era: a layer based on multi-modal deep neural networks that can reduce the false-positive events selected by the trigger by over one order of magnitude while retaining 99% of signal events, a full-event simulation algorithm based on recurrent generative adversarial networks that has potential to replace traditional simulation method while being five orders of magnitude faster, and a fast simulation algorithm for specific analyses based on encoder-decoder architecture that would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:High energy physics; Higgs boson; Supersymmetry; Large Hadron Collider; CMS Collaboration; CERN; Machine learning; Deep learning
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Physics
Awards:CMS Achievement Award 2019
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Spiropulu, Maria
Thesis Committee:
  • Golwala, Sunil (chair)
  • Cheung, Clifford W.
  • Spiropulu, Maria
  • Weinstein, Alan Jay
Defense Date:23 September 2021
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0011925
Record Number:CaltechTHESIS:10012021-210141406
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Ch. 12 adapted for Ch. 5 adapted for Ch. 8 adapted for Ch. 8 adapted for Ch. 6 adapted for Ch. 6 adapted for Ch. 11 adapted for Ch. 8 adapted for Ch. 10 adapted for Ch. 11
Nguyen, Thong Quang0000-0003-3954-5131
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14380
Deposited By: Thong Nguyen
Deposited On:08 Oct 2021 14:51
Last Modified:27 Oct 2021 22:07

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