Project:	Particle Physics on the Energy Frontier: Novel Reconstruction Algorithms at a Muon Collider
Discipline:	Physics
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:	A muon collider is a compelling next-generation facility, offering both an unprecedented energy reach and precision measurement capabilities within a compact geographical footprint. However, a major challenge is the overwhelming beam-induced background from muon decays and their interactions with detector material, which complicates event reconstruction and analysis. This project will focus on developing advanced reconstruction algorithms, including harnessing machine learning-based methods, to accurately reconstruct physics objects while mitigating backgrounds.
References:	Towards a Muon Collider: https://arxiv.org/pdf/2303.08533 MAIA: A new detector concept for a 10 TeV muon collider: https://arxiv.org/abs/2502.00181 Full Detector Simulation with Unprecedented Background Occupancy at a Muon Collider: https://cds.cern.ch/record/2891208/files/Publication.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.