RESUMO
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.
RESUMO
The quality of blood donated or purchased may be not only inferior from a recipient or user point of view, but donation may also be injurious to the health of the donor. A study of 1215 paid donors suggests that the current screening guidelines for all donors may not be adequate. More extensive haematological study of these donors shows that abnormalities of individual haematological parameters may range from 4.3% to as high as 29.5% deending on the donor population and the quality of the screening examination. The study concludes that the donor should be better protected.
