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Weakly supervised classification in high energy physics

  • Lucio Mwinmaarong Dery
  • Benjamin Nachman
  • Francesco RubboEmail author
  • Ariel Schwartzman
Open Access
Regular Article - Theoretical Physics

Abstract

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Keywords

Jets 

Notes

Open Access

This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Physics DepartmentStanford UniversityStanfordU.S.A.
  2. 2.Physics DivisionLawrence Berkeley National LaboratoryBerkeleyU.S.A.
  3. 3.SLAC National Accelerator LaboratoryStanford UniversityMenlo ParkU.S.A.

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