Project:	Particle Physics on the Energy Frontier: Anomaly Detection with the CMS Experiment
Disciplines:	Physics, Data Science
Mentor:	Harvey Newman, Professor or Physics, (PMA), newman@hep.caltech.edu
AO Contact:	Kiley Kennedy, kileykennedy@princeton.edu
Background:	There are multiple opportunities available at the intersection of experimental particle physics and artificial intelligence (AI) / machine learning (ML) for improving analysis and computing techniques, either with the CMS experiment at the Large Hadron Collider (LHC) at CERN or at a potential muon collider. These opportunities are through the SURF program for Caltech students only and are also available for term-time research (e.g. for research units in Ph 172).
Description:	Many compelling theories suggest that new physics beyond the Standard Model (BSM) should be accessible at the LHC; however, there is no definitive prediction for what form these theories might take. To address this, the CMS experiment recently deployed the novel anomaly detection trigger, CICADA, which selects anomalous collision events, providing model-independent sensitivity to new physics. This project will focus on analyzing CICADA-selected events, including leveraging unsupervised deep learning techniques, to identify patterns and deviations that could provide hints of BSM physics.
References:	Level-1 Trigger Calorimeter Image Convolutional Anomaly Detection Algorithm: https://cds.cern.ch/record/2879816/files/DP2023_086.pdf Model-Independent Real-Time Anomaly Detection at the CMS Level-1 Calorimeter Trigger with CICADA: https://cds.cern.ch/record/2917884/files/DP2024_121.pdf
Student Requirements:	Introductory physics courses and coding experience. Familiarity with high energy physics, machine learning and/or previous project experience is a plus.
Programs:	This AO can be done under the following programs:   Program    Available To        SURF    Caltech students only  Click on a program name for program info and application requirements.