Convolutional Neural Networks for Direct Detection of Dark Matter

Abstract

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\%.

arXiv comment17 figures, 4 tables (matches the published version)

References in corpus (20)

• Scikit-learn: Machine Learning in Python
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Machine learning and the physical sciences
• Dark Matter Search Results from a One Tonne$\times$Year Exposure of XENON1T
• First Dark Matter Search Results from the XENON1T Experiment
• The XENON100 Dark Matter Experiment
• DarkSide-50 532-day Dark Matter Search with Low-Radioactivity Argon
• The XENON1T Dark Matter Experiment
• Cosmological constraints with deep learning from KiDS-450 weak lensing maps
• Design and Performance of the XENON10 Dark Matter Experiment
• A deep learning approach to cosmological dark energy models
• XENON1T Dark Matter Data Analysis: Signal & Background Models, and Statistical Inference
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• A Global Analysis of Light and Charge Yields in Liquid Xenon
• XENON1T Dark Matter Data Analysis: Signal Reconstruction, Calibration and Event Selection
• From Dark Matter to Galaxies with Convolutional Networks
• Deep Learning the Morphology of Dark Matter Substructure
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Global Analysis of Dark Matter Simplified Models with Leptophobic Spin-One Mediators using MasterCode
• Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection

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