Evaluation of Decision Trees Algorithms for Position Reconstruction in Argon Dark Matter Experiment

Nowadays, Dark Matter search constitutes one of the most challenging scientific activity. During the last decades several detectors have been developed to evidence the signal of interactions between Dark Matter and ordinary matter. The Argon Dark Matter detector, placed in the Canfranc Underground Laboratory in Spain is the first ton-scale liquid-Ar experiment in operation for Dark Matter direct detection. In parallel to the development of other engineering issues, computational methods are being applied to maximize the exploitation of generated data. In this work, two algorithms based on decision trees —Generalized Boosted Regression Models and Random Forests— are employed to reconstruct the position of the interaction in Argon Dark Matter detector. These two algorithms are confronted to a Montecarlo data set reproducing the physical behaviour of Argon Dark Matter detector. In this work, an in-depth study of the position reconstruction of the interaction is performed for both algorithms, including a study of the distribution of errors.