Punzi-loss:: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles.

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.

Observation of D

We report the first observation of the radiative charm decay D^{0}→ρ^{0}γ and the first search for CP violation in decays D^{0}→ρ^{0}γ, ϕγ, and K[over ¯]^{*0}(892)γ, using a data sample of 943 fb^{-1} collected with the Belle detector at the KEKB asymmetric-energy e^{+}e^{-} collider. The branching fraction is measured to be B(D^{0}→ρ^{0}γ)=(1.77±0.30±0.07)×10^{-5}, where the first uncertainty is statistical and the second is systematic. The obtained CP asymmetries A_{CP}(D^{0}→ρ^{0}γ)=+0.056±0.152±0.006, A_{CP}(D^{0}→ϕγ)=-0.094±0.066±0.001, and A_{CP}(D^{0}→K[over ¯]^{*0}γ)=-0.003±0.020±0.000 are consistent with no CP violation. We also present an improved measurement of the branching fractions B(D^{0}→ϕγ)=(2.76±0.19±0.10)×10^{-5} and B(D^{0}→K[over ¯]^{*0}γ)=(4.66±0.21±0.21)×10^{-4}.