The advancement of technology has led to rampant growth in data collection across almost every field, including astrophysics, with researchers turning to machine learning to process and analyze this data. One prominent example of @@ -31,19 +31,4 @@
The detection of Dark Matter (DM) remains a significant challenge in particle -physics. This study exploits advanced machine learning models to improve -detection capabilities of liquid xenon time projection chamber experiments, -utilizing state-of-the-art transformers alongside traditional methods like -Multilayer Perceptrons and Convolutional Neural Networks. We evaluate various -data representations and find that simplified feature representations, -particularly corrected S1 and S2 signals, retain critical information for -classification. Our results show that while transformers offer promising -performance, simpler models like XGBoost can achieve comparable results with -optimal data representations. We also derive exclusion limits in the -cross-section versus DM mass parameter space, showing minimal differences -between XGBoost and the best performing deep learning models. The comparative -analysis of different machine learning approaches provides a valuable reference -for future experiments by guiding the choice of models and data representations -to maximize detection capabilities.
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