000002612 001__ 2612
000002612 005__ 20211001084638.0
000002612 037__ $$aBELLE2-PUB-TE-2021-001
000002612 041__ $$aeng
000002612 100__ $$aP. Feichtinger
000002612 245__ $$aPunzi-loss, a non-differentiable metric approximation for sensitivity optimization in the search for new particles
000002612 260__ $$a$$c2021-08-25
000002612 300__ $$amult. p
000002612 520__ $$aWe present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimization of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilizes this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalizes well to mass hypotheses for which it was not trained. Our result constitutes a step towards fully differentiable analyses in particle physics.  This work is implemented using PyTorch and we provide users full access to a public repository containing all the codes.
000002612 700__ $$aH. Haigh
000002612 700__ $$aG. Inguglia
000002612 700__ $$aJ. Kahn
000002612 8560_ $$fgianluca.inguglia@oeaw.ac.at
000002612 8564_ $$uhttps://docs.belle2.org/record/2612/files/BELLE2-PUB-TE-2021-001-V3.pdf