Background rejection in NEXT using deep neural networks

Article

Abstract: We investigate the potential of using deep learning techniques to reject background events in search

Palabras claves:

Analysis and statistical methods, calibration and fitting methods, cluster finding, Double-beta decay detectors, pattern recognition, Time projection chambers

Autores:

Álvarez V., Azevedo C.D.R., Benlloch-Rodríguez J.M., Borges F.I.G., Botas A., Cárcel S., Carrión J.V., Cebrián S., Cervera A., Conde C.A.N., Díaz J., Diesburg M., dos Santos J.M.F., Esteve R., Farbin A., Fernandes L.M.P., Ferrario P., Ferreira A.L., Freitas E.D.C., Goldschmidt A., Gómez-Cadenas J.J., González-Díaz D., Gutierrez R.M., H. Yepes-Ramirez, Hauptman J., Henriques C.A.O., Herrero V., Jones B., Labarga L., Laing A., Lebrun P., Liubarsky I., López-March N., Lorca D., Losada M., Martín-Albo J., Martínez A., Martínez-Lema G., Monrabal F., Monteiro C.M.B., Mora F.J., Morata J.A.H., Moutinho L.M., Nebot-Guinot M., Novella P., Nygren D., Palmeiro B., Para A., Pérez J., Querol M., Renner J., Ripoll L., Rodríguez J., Santos F.P., Serra L., Shuman D., Simón A., Sofka C., Sorel M., Toledo J.F., Torrent J., Tsamalaidze Z., Veloso J.F.C.A., Vidal J.M., Webb R., White J.T., Yahlali N.

Fuentes:

scopus