ABSTRACT
We 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 minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.
ABSTRACT
We have searched for the decay B+-->omegal(+)nu (l=e or mu) in 78 fb(-1) of Upsilon(4S) data (85x10(6)BB events) accumulated with the Belle detector. The final state is fully reconstructed using the omega decay into pi(+)pi(-)pi(0), combined with detector hermeticity to estimate the neutrino momentum. A signal of 414+/-125 events is found in the data, corresponding to a branching fraction of (1.3+/-0.4+/-0.2+/-0.3)x10(-4), where the first two errors are statistical and systematic, respectively. The third error reflects the estimated form-factor uncertainty.
