Jean-Roch Vlimant

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M. Pierini

European Organization for Nuclear Research

Anders Andreassen

Google (United States)

Kees Benkendorfer

Harvard University Press

Louis Vaslin

Institut National de Physique Nucléaire et de Physique des Particules

Charanjit K. Khosa

University of Sussex

O. Amram

Fermi National Accelerator Laboratory

Philip Harris

Massachusetts Institute of Technology

Manuel Szewc

University of Cincinnati

J. Duarte

University of California, San Diego

Sang Eon Park

Pusan National University

Cooperative Institutions

A. Alikhanyan National Laboratory

Fermi National Accelerator Laboratory

Institute of High Energy Physics

University of Adelaide

European Organization for Nuclear Research

University of Antwerp

Massachusetts Institute of Technology

Lawrence Berkeley National Laboratory

University of Chicago

Atlas Scientific (United States)

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The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

arXiv (Cornell University) (2021)

Gregor Kasieczka Benjamin Nachman David Shih O. Amram Anders Andreassen

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.

Leverage (statistics)

Benchmark (surveying)

Anomaly (physics)

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Citations (34)

Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

arXiv (Cornell University) (2020)

Oliver Knapp G. Dissertori Olmo Cerri T. Q. Nguyen Jean-Roch Vlimant

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the t-tbar experimental signature at the LHC.

Anomaly (physics)

Signature (topology)

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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

HAL (Le Centre pour la Communication Scientifique Directe) (2022)

Phiala E. Shanahan K. Terao Daniel Whiteson Gert Aarts Andreas Adelmann

Frontier

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Citations (6)