1 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
VERaiPHY -- Validation & Evaluation for Robust AI in PHYsics
Gaia Grosso, Ramon Winterhalder, Lydia Brenner +2
Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantifi…
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
Gaia Grosso, Vinicius Mikuni, Lukas Heinrich
Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems g…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…