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Title: Improving inference with matrix elements and machine learning

Abstract

Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.

Authors:
 [1];  [1];  [2]
  1. New York Univ. (NYU), NY (United States)
  2. SLAC National Accelerator Lab., Menlo Park, CA (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1647463
Grant/Contract Number:  
AC02-76SF00515; ACI-1450310; OAC-1836650; PHY-1505463; OAC-1841471; PHY-1620638
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Modern Physics A
Additional Journal Information:
Journal Volume: 35; Journal Issue: 15n16; Journal ID: ISSN 0217-751X
Publisher:
World Scientific
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Brehmer, Johann, Cranmer, Kyle, and Kling, F. Improving inference with matrix elements and machine learning. United States: N. p., 2020. Web. https://doi.org/10.1142/s0217751x20410080.
Brehmer, Johann, Cranmer, Kyle, & Kling, F. Improving inference with matrix elements and machine learning. United States. https://doi.org/10.1142/s0217751x20410080
Brehmer, Johann, Cranmer, Kyle, and Kling, F. Thu . "Improving inference with matrix elements and machine learning". United States. https://doi.org/10.1142/s0217751x20410080. https://www.osti.gov/servlets/purl/1647463.
@article{osti_1647463,
title = {Improving inference with matrix elements and machine learning},
author = {Brehmer, Johann and Cranmer, Kyle and Kling, F.},
abstractNote = {Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.},
doi = {10.1142/s0217751x20410080},
journal = {International Journal of Modern Physics A},
number = 15n16,
volume = 35,
place = {United States},
year = {2020},
month = {6}
}

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