Exploring the Dark Sector: Searches for Long-Lived Particles and Machine Learning Innovations at the LHC
Dr. Daniel Diaz
Postdoctoral Scholar
UCSD
The Standard Model (SM) of particle physics successfully describes many observed phenomena but leaves fundamental questions unanswered, including the nature of dark matter and the matter-antimatter asymmetry. These gaps motivate searches for physics beyond the Standard Model (BSM). This talk will highlight efforts to probe dark sectors via searches for long-lived particles (LLPs) at the Large Hadron Collider (LHC). LLPs, predicted by many BSM theories, present unique experimental challenges due to their extended lifetimes and unconventional signatures. I will discuss novel search strategies and detector techniques developed to enhance sensitivity to LLPs, including the use of machine learning (ML) algorithms for both online and offline data analysis. The transition to the High Luminosity LHC (HL-LHC) era introduces additional opportunities and challenges, requiring advanced ML-driven triggers to effectively filter data in real time. I will present key results from past searches, outline upgrades to the CMS Level-1 trigger, and describe plans to expand LLP searches to lower masses and longer lifetimes.