@article{10272/25656,
year = {2016},
month = {5},
url = {https://hdl.handle.net/10272/25656},
abstract = {The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported},
organization = {The authors are grateful to the FAZIA collaboration [27] for providing part of the experimental data used in the present study. This work has been supported by the Spanish Ministerio de Economía y Competitividad under the grant FPA2014-59954-C3-1-P, by the Andalusian Conserjería de Innovación, Ciencia y Empresa, under the grants FQM 4964 and P10-TIC-6311, and by the European Union Seventh Framework Programme FP/2007-2013 under Grant Agreement n. 262010-EN-SAR},
publisher = {Elsevier},
title = {Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors},
doi = {10.1016/j.nima.2016.05.107},
author = {Flores Garrido, Juan Luis and Martel Bravo, Ismael and Jiménez Naharro, Raúl and Salmerón Revuelta, Patricio and Gómez Galán, Juan Antonio},
}