Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC
[Sztuczna inteligencja] Autorzy: Donatella Genovese, Philip Ploner, Simone Scardapane i 2 więcej
Anomaly detection, the identification of collision events that deviate from Standard Model expectations, is one of the most promising model-agnostic strategies for new-physics searches at the Large Hadron Collider. Searching for rare events in learned low-dimensional representations removes the need to commit to hand-crafted observables a priori, but sacrifices the immediate physical interpretation those observables provide. For anomaly detection to enter a discovery pipeline, flagged events must therefore be diagnosed locally, both to distinguish potential new physics from mismodeling or detector effects and to identify observables for targeted follow-up searches. We evaluate supervised and self-supervised neural embedding strategies on COLLIDE-2V, a 750-million-event dataset simulated under realistic High-Luminosity LHC conditions, using the reconstruction error of an autoencoder trai…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja