Name: Anomaly detection techniques at the LHC
Start: 2025-04-23T13:00:00+02:00
End: 2025-04-23T14:00:00+02:00
Location: In-person

Description

Machine learning–driven anomaly detection has rapidly emerged as a powerful tool to enhance the traditional search strategies used in ATLAS and CMS. By harnessing recent progress in unsupervised learning, these techniques boost sensitivity to potential new physics in a model-independent way - broadening coverage across diverse final states while requiring fewer resources. In this seminar, I will outline several of the common strategies for anomaly detection before highlighting the areas that we are working on at IFAE.

Organized by

Giada Caneva, Elia Bertoldo, Francesco Sciotti

Anomaly detection techniques at the LHC

by Jack Harrison (IFAE)

IFAE Seminar Room

In-person