3 papers
hep-ph2026
Covariant Contrastive Learning for Uncertainty-Aware Anomaly Detection
Shelley Tong, Philip Harris, Gaia Grosso
Machine-learning-based anomaly detection (AD) offers a promising, model-agnostic alternative to traditional LHC analyses, allowing to search for many signals at once. Recent advanc…
hep-ex2024
Learning to Reconstruct Quirky Tracks
Qiyu Sha, Daniel Murnane, Max Fieg +4
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some pot…
hep-ph2023
New Physics in Single Resonant Top Quarks
Shelley Tong, James Corcoran, Max Fieg +2
Searches for new physics in the top quark sector are of great theoretical interest, yet some powerful avenues for discovery remain unexplored. We characterize the expected statisti…