TY  - JOUR
AU  - NEXT Collaboration (Kekic, M. et al
AU  - Benlloch-Rodriguez, J. M.
AU  - Carcel, S.
AU  - Carrion, J. V.
AU  - Diaz, J.
AU  - Felkai, R.
AU  - Lopez-March, N.
AU  - Martin-Albo, J.
AU  - Martinez, A.
AU  - Martinez-Lema, G.
AU  - Martinez-Vara, M.
AU  - Muñoz Vidal, J.
AU  - Novella, P.
AU  - Palmeiro, B.
AU  - Querol, M.
AU  - Renner, J.
AU  - Romo-Luque, C.
AU  - Sorel, M.
AU  - Uson, A.
AU  - Yahlali, N.
PY  - 2021
DA  - 2021//
TI  - Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
T2  - J. High Energy Phys.
JO  - Journal of High Energy Physics
SP  - 189
EP  - 22pp
VL  - 01
IS  - 1
PB  - Springer
KW  - Dark Matter and Double Beta Decay (experiments)
AB  - Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.
SN  - 1029-8479
UR  - https://arxiv.org/abs/2009.10783
UR  - https://doi.org/10.1007/JHEP01(2021)189
DO  - 10.1007/JHEP01(2021)189
LA  - English
N1  - WOS:000616730800001
ID  - NEXTCollaborationKekic_etal2021
ER  -