Deep Neural Network as an alternative to Boosted Decision Trees for PID
Abstract
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.
Keywords
Cite
@article{arxiv.2104.14045,
title = {Deep Neural Network as an alternative to Boosted Decision Trees for PID},
author = {Denis Stanev and Riccardo Riva and Michele Umassi},
journal= {arXiv preprint arXiv:2104.14045},
year = {2021}
}
Comments
9 pages, 6 figures. The code can be found at https://github.com/Denis-Stanev/DNN-vs-BDT . The data file can be found here: https://archive.ics.uci.edu/ml/datasets/MiniBooNE+particle+identification