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New Physics Tools for Discovery, a New Era of Timing Detector, and Lepton Flavor Universality Test at CMS


Cerri, Olmo (2023) New Physics Tools for Discovery, a New Era of Timing Detector, and Lepton Flavor Universality Test at CMS. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/bqsn-sp82.


The field of particle and fundamental physics finds itself now in a peculiar situation. The established Standard Model accurately predicts most of the observations, but several compelling reasons motivate a need for an extension of the current theory. In this thesis, I focus my research on facing the current situation of the field in a diversified threefold manner.

First, I develop methods based on physics-driven machine learning algorithms, with a particular focus on developing a model-independent tagger for unexpected events using artificial neural networks. This study shows how model-independent new physics triggers, possibly trained on real data, can select a low rate stream of events able to explore new physics processes up to a 10-100 pb cross section and can create a special dataset of rare unexpected events. Other important results from this body of work include the first application of the proposed anomaly detection strategy to real data, the use of graph neural networks to improve current pileup mitigation algorithms, the development of jet taggers based on the interaction network, and analysis-specific fast simulation.

Second, I focus on the methodological and hardware development of the MIP Timing Layer that is expected to upgrade CMS in preparation for HL-LHC. My seminal study demonstrates the possibility of using time-of-flight information to perform particle identification, which has a significant impact on heavy stable charged particle searches. This work introduces how to measure time-of-flight at CMS, a strategy for particle identification, and an algorithm to locate vertices in space and time. I also participated in the sensor testing and test beam operation. In particular, I conducted a study about the design and prototype of the detector modules' thermal behavior that shows how different geometries could lead to cooling differences of a few K.

Last, I direct my attention towards CMS's first lepton flavor universality tests with B meson decays. Using a dataset acquired thanks to a custom design trigger, I independently develop the measurement of the R(D*) ratio, a parameter whose tensions between the predictions and observation have drawn remarkable attentions. I oversaw the complete mature state of the analysis, from the Monte Carlo simulation to the fitting procedure. Further collaboration-wide efforts are still required, but I demonstrate the expected sensitivity of about 15% using an Asimov dataset.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:High Energy Physics
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Physics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Spiropulu, Maria
Thesis Committee:
  • Weinstein, Alan Jay (chair)
  • Cheung, Clifford W.
  • Patterson, Ryan B.
  • Wise, Mark B.
  • Spiropulu, Maria
Defense Date:3 August 2022
Record Number:CaltechTHESIS:09132022-001004681
Persistent URL:
Related URLs:
URLURL TypeDescription "Identification of long-lived charged particles using time-of-flight systems at the upgraded LHC detectors." "Variational autoencoders for new physics mining at the Large Hadron Collider." "Topology classification with deep learning to improve real-time event selection at the LHC." "Adversarially Learned Anomaly Detection on CMS Open Data: Re-discovering the top quark." "Pileup mitigation at the Large Hadron Collider with graph neural networks." "JEDI-net: A jet identification algorithm based on interaction networks." "Interaction networks for the identification of boosted..." "Analysis-specific fast simulation at the LHC with deep Learning."
Cerri, Olmo0000-0002-2191-0666
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15019
Deposited By: Olmo Cerri
Deposited On:30 Nov 2022 22:30
Last Modified:07 Dec 2022 16:51

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