Journal Article

FZJ-2022-00863

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Adversarial domain adaptation to reduce sample bias of a high energy physics event classifier

Clavijo, J. M. (Corresponding author)DESY* ; Glaysher, P. ; Jitsev, J.FZJ* ; Katzy, J. M. (Corresponding author)DESY*

2022
IOP Publishing Bristol

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Please use a persistent id in citations: http://hdl.handle.net/2128/30460  doi:10.1088/2632-2153/ac3dde

Abstract: We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimizing the difference in response of the network to background samples originating from different Monte Carlo models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the Large Hadron Collider with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification and discuss implications and limitations of the method.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. ZT-I-PF-5-3 - Deep Generative models for fast and precise physics Simulation (DeGeSim) (2020_ZT-I-PF-5-3) (2020_ZT-I-PF-5-3)

Appears in the scientific report 2022

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