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Catarina Doglioni

Caterina Doglioni

Affiliated

Catarina Doglioni

Benchmark data and model independent event classification for the large hadron collider

Author

  • Thea Aarrestad
  • Melissa van Beekveld
  • Marcella Bona
  • Antonio Boveia
  • Sascha Caron
  • Joe Davies
  • Andrea De Simone
  • Caterina Doglioni
  • Javier M. Duarte
  • Amir Farbin
  • Honey Gupta
  • Luc Hendriks
  • Lukas Heinrich
  • James Howarth
  • Pratik Jawahar
  • Adil Jueid
  • Jessica Lastow
  • Adam Leinweber
  • Judita Mamuzic
  • Erzsébet Merényi
  • Alessandro Morandini
  • Polina Moskvitina
  • Clara Nellist
  • Jennifer Ngadiuba
  • Bryan Ostdiek
  • Maurizio Pierini
  • Baptiste Ravina
  • Roberto R. de Austri
  • Sezen Sekmen
  • Mary Touranakou
  • Marija Vaškevičiūte
  • Ricardo Vilalta
  • Jean Roch Vlimant
  • Rob Verheyen
  • Martin White
  • Eric Wulff
  • Erik Wallin
  • Kinga A. Wozniak
  • Zhongyi Zhang
Show all

Summary, in English

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

Department/s

  • Particle and nuclear physics
  • eSSENCE: The e-Science Collaboration
  • MAX IV Laboratory

Publishing year

2022-01-01

Language

English

Publication/Series

SciPost Physics

Volume

12

Issue

1

Document type

Journal article

Publisher

SciPost

Topic

  • Accelerator Physics and Instrumentation

Status

Published

ISBN/ISSN/Other

  • ISSN: 2542-4653