Events on Tuesday, July 30th, 2024
- Search for exotic Higgs boson decays with CMS and fast machine learning solutions for the LHC
- Time: 2:00 pm - 4:00 pm
- Place: 5310 Chamberlin Hall
- Speaker: Ho Fung Tsoi, Physics PhD Graduate Student
- Abstract: The first part of this thesis presents a search for new physics with the CMS experiment. There is still potential for discoveries beyond the Standard Model in the scalar sector, which could manifest as exotic Higgs boson decays into light pseudoscalars. This search targets such decays, focusing on pseudoscalar masses ranging from 12 and 60 GeV, in final states where one pseudoscalar decays into two b quarks and the other into two $\tau$ leptons or two muons. The analysis is based on a dataset of proton-proton collisions at $\sqrt{s}=13$ TeV, collected by the CMS detector during LHC Run 2, with an integrated luminosity of 138 $\text{fb}^{-1}$. Dedicated neural networks are used to distinguish between signal and background, significantly enhancing sensitivity. The results are presented as exclusion limits at 95\% confidence level on the model-independent branching ratio and are interpreted within two-Higgs doublet models augmented by a singlet. The second part of this thesis presents machine learning methods to enhance overall sensitivity in the low-latency domain for the LHC experiments. A novel machine learning-based trigger algorithm is developed, using anomaly detection to search for new physics in a model-agnostic manner as close to the raw collision data as possible. This anomaly detection trigger is sensitive to a wide range of both conventional and unconventional physics signals and has an inference latency of O(100) ns on an FPGA. It is deployed during Run 3 in the CMS Level-1 trigger system, which processes the first round of real-time event selection from collision data at a rate of 40 MHz. Additionally, a novel model compression method using symbolic regression is developed to accelerate machine learning inference to nanosecond speeds on FPGAs. We demonstrate its potential to significantly reduce the computational costs of machine learning algorithms while maintaining performance comparable to that of neural networks. These advancements are crucial for meeting the sensitivity and computational demands of resource-constrained environments such as the LHC experiments.
- Host: Sridhara Dasu