9-13 July 2018
Sofia, Bulgaria
Europe/Sofia timezone

The use of adversaries for optimal neural network configuration

10 Jul 2018, 15:00
Hall 9 (National Palace of Culture)

Hall 9

National Palace of Culture

presentation Track 6 – Machine learning and physics analysis T6 - Machine learning and physics analysis


Prof. Martin Sevior (University of Melbourne)


Data from B-physics experiments at the KEKB collider have a substantial background from $e^{+}e^{-}\to q \bar{q}$ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the neural network develops a substantial correlation with the $\Delta E$ kinematic variable used to distinguish signal from background in the final fit due to its relationship with the input variables. The effect of this correlation is counter-acted by deploying an adversarial neural network. Overall the adversarial deep neural network performs better than an unoptimised commercial package, NeuroBayes.

Primary authors

Mr Anton Hawthorne-Gonzalvez (University of Melbourne) Prof. Martin Sevior (University of Melbourne)

Presentation materials