  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.
