  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\%.
