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Journal of High Energy Physics

, 2017:51 | Cite as

Pileup Mitigation with Machine Learning (PUMML)

  • Patrick T. Komiske
  • Eric M. Metodiev
  • Benjamin Nachman
  • Matthew D. Schwartz
Open Access
Regular Article - Theoretical Physics

Abstract

Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.

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

  • Patrick T. Komiske
    • 1
  • Eric M. Metodiev
    • 1
  • Benjamin Nachman
    • 2
  • Matthew D. Schwartz
    • 3
  1. 1.Center for Theoretical Physics, Massachusetts Institute of TechnologyCambridgeU.S.A.
  2. 2.Physics DivisionLawrence Berkeley National LaboratoryBerkeleyU.S.A.
  3. 3.Department of PhysicsHarvard UniversityCambridgeU.S.A.

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