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Title: Pileup Mitigation with Machine Learning (PUMML)

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.

Authors:
 [1];  [1];  [2];  [3]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Harvard Univ., Cambridge, MA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25); USDOE Office of Science (SC), Nuclear Physics (NP) (SC-26)
OSTI Identifier:
1485069
Grant/Contract Number:  
AC02-05CH11231; SC0013607; SC0011090; SC0012567
Resource Type:
Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2017; Journal Issue: 12; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Jets

Citation Formats

Komiske, Patrick T., Metodiev, Eric M., Nachman, Benjamin, and Schwartz, Matthew D. Pileup Mitigation with Machine Learning (PUMML). United States: N. p., 2017. Web. doi:10.1007/jhep12(2017)051.
Komiske, Patrick T., Metodiev, Eric M., Nachman, Benjamin, & Schwartz, Matthew D. Pileup Mitigation with Machine Learning (PUMML). United States. doi:10.1007/jhep12(2017)051.
Komiske, Patrick T., Metodiev, Eric M., Nachman, Benjamin, and Schwartz, Matthew D. Tue . "Pileup Mitigation with Machine Learning (PUMML)". United States. doi:10.1007/jhep12(2017)051. https://www.osti.gov/servlets/purl/1485069.
@article{osti_1485069,
title = {Pileup Mitigation with Machine Learning (PUMML)},
author = {Komiske, Patrick T. and Metodiev, Eric M. and Nachman, Benjamin and Schwartz, Matthew D.},
abstractNote = {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.},
doi = {10.1007/jhep12(2017)051},
journal = {Journal of High Energy Physics (Online)},
number = 12,
volume = 2017,
place = {United States},
year = {2017},
month = {12}
}

Journal Article:
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Cited by: 25 works
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Figures / Tables:

Figure 1 Figure 1: An illustration of the PUMML framework. The input is a three-channel image: blue/purple represents charged radiation from the leading vertex, green is charged pileup radiation, and yellow/orange/red is the total neutral radiation. The resolution of the charged images is higher than for the neutral one. These images aremore » fed into a convolutional layer with several filters whose value at each pixel is a function of a patch around that pixel location in the input images. The output is an image combining the pixels of each filter to one output pixel.« less

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    Works referencing / citing this record:

    Quark jet versus gluon jet: fully-connected neural networks with high-level features
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    Learning representations of irregular particle-detector geometry with distance-weighted graph networks
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    Quark jet versus gluon jet: fully-connected neural networks with high-level features
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    • Science China Physics, Mechanics & Astronomy, Vol. 62, Issue 9
    • DOI: 10.1007/s11433-019-9390-8

    Learning representations of irregular particle-detector geometry with distance-weighted graph networks
    journal, July 2019