PHYSTAT-Anomalies

Session

Session 2a: Chair: Ann Lee

25 May 2022, 17:00

Presentation materials

video_2a.mp4

6. Challenges of anomaly detection with LHC data

Ines Ochoa (LIP Laboratorio de Instrumentacao e Fisica Experimental de Particulas (PT))

25/05/2022, 17:00

ABSTRACT:
In recent years, there have been many proposed methodologies for machine learning anomaly detection at the LHC, such as those reported in the LHC Olympics and Dark Machines community reports.  The first search using machine-learning anomaly detection was performed by ATLAS in the dijet final state, a fully data-driven analysis that uses the Classification Without Labels method and...

7. LHC Olympics

Gregor Kasieczka (Hamburg University)

25/05/2022, 17:45

ABSTRACT:
We are at the beginning of a new era of data-driven, model-agnostic new physics searches at colliders that combine recent breakthroughs in anomaly detection and machine learning. This contribution will report on the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D...

12. Discussion

25/05/2022, 18:30