About
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created 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 dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. Based on https://arxiv.org/abs/2101.08320 ..
0:00:00 Slide 1
0:03:12 Slide 2
0:06:23 Slide 3
0:09:34 Slide 4
0:12:45 Slide 5
0:15:56 Slide 6
0:19:07 Slide 7
0:22:18 Slide 8
0:25:29 Slide 9
0:28:40 Slide 10
The talk
Credits and licence
- Institution
- CERN
- Department
- LPCC Workshops
- Speaker
- Ben Nachman
- Year
- 2021
- Language
- English
- Subjects
- Physics
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed