Improving inference with matrix elements and machine learning

Journal Article · 04 June 2020 · International Journal of Modern Physics A

DOI: https://doi.org/10.1142/s0217751x20410080 · OSTI ID:1647463

Brehmer, Johann ^[1]; Cranmer, Kyle ^[1]; Kling, F. ^[2]

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1. New York Univ. (NYU), NY (United States)
2. SLAC National Accelerator Lab., Menlo Park, CA (United States)

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.

• Research Organization: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: AC02-76SF00515; ACI-1450310; OAC-1836650; PHY-1505463; OAC-1841471; PHY-1620638
• OSTI ID: 1647463
• Journal Information: International Journal of Modern Physics A, Vol. 35, Issue 15n16; ISSN 0217-751X
• Publisher: World ScientificCopyright Statement
• Country of Publication: United States
• Language: English