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

Deep Neural Network as an alternative to Boosted Decision Trees for PID