Abstract: 

Anomaly detection techniques are playing an increasingly central role in the search for new physics at the Large Hadron Collider (LHC), where the rarity and unpredictability of signals beyond the Standard Model challenge traditional analysis strategies. In this talk, I will present recent developments in machine learning–based anomaly detection for high-energy physics, with a focus on model-agnostic approaches that aim to uncover unexpected signatures in collision data. I will discuss how these algorithms are currently being used to probe the Standard Model at the LHC, and how next-generation and future searches might be deploying such algorithms, from FPGA-accelerated algorithms to quantum computing strategies.

 

 

Dr Benedikt Maier is an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at Imperial College London, leading the EPIGRAPHY network to advance deep learning solutions for real-time edge computing in particle physics experiments at the LHC. As co-convenor of the CMS Exotics physics group, he specializes in searches for new physics and dark matter, pioneering machine learning methods for both data analysis and large-scale computing. Dr Maier has played a pivotal role in managing vast data resources within the CMS collaboration’s international computing grid. His achievements have earned him the CMS Young Researcher Prize, highlighting his sustained contributions at the intersection of high-energy physics and artificial intelligence.