Novel Jet Observables from Machine Learning

17 Jul 2018, 14:30
Charpak Amphitheater (Paris)

Charpak Amphitheater


UPMC (Jussieu) Campus


Kaustuv Datta (ETHZ - ETH Zurich)


Ref [1] presented a method to construct novel observables exclusively from information identified by the machine, by using a parametrization of the N-body phase space coordinates at the point of saturation of discrimination power. We have now studied how to extend this approach in an automated way to higher N-body phase space. We use boosted Z' vs QCD discrimination as a testing ground to present a machine learning framework that adversarially carries out the task of developing the optimal discriminant for the problem. Following on from a long string of results that have improved upon previous N-prong taggers, this technique has important implications for future development of optimal discrimination observables.

[1] K. Datta and A. Larkoski, Novel Jet Observables from Machine Learning, JHEP 03, 086 (2018)

Primary author

Kaustuv Datta (ETHZ - ETH Zurich)


Ben Nachman (University of California Berkeley (US)) Andrew Larkoski (Reed Collge)

Presentation Materials