  In this paper we recreate, and improve, the binary classification method for
particles proposed in Roe et al. (2005) paper "Boosted decision trees as an
alternative to artificial neural networks for particle identification". Such
particles are tau neutrinos, which we will refer to as background, and
electronic neutrinos: the signal we are interested in. In the original paper
the preferred algorithm is a Boosted decision tree. This is due to its low
effort tuning and good overall performance at the time. Our choice for
implementation is a deep neural network, faster and more promising in
performance. We will show how, using modern techniques, we are able to improve
on the original result, both in accuracy and in training time.
