TY  - JOUR
AU  - Khosa, C. K.
AU  - Mars, L.
AU  - Richards, J.
AU  - Sanz, V.
PY  - 2020
DA  - 2020//
TI  - Convolutional neural networks for direct detection of dark matter
T2  - J. Phys. G
JO  - Journal of Physics G
SP  - 095201
EP  - 20pp
VL  - 47
IS  - 9
PB  - Iop Publishing Ltd
KW  - dark matter
KW  - dark matter detection
KW  - neural networks
KW  - xenon1T
KW  - WIMPs
AB  - The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
SN  - 0954-3899
UR  - https://arxiv.org/abs/1911.09210
UR  - https://doi.org/10.1088/1361-6471/ab8e94
DO  - 10.1088/1361-6471/ab8e94
LA  - English
N1  - WOS:000555607800001
ID  - Khosa_etal2020
ER  -