A fast and time-efficient machine learning approach to dark matter searches in compressed mass scenario

Abstract

Various analyses for searching for the signature of SUSY or exotic particles have been carried out by the experiments at CERN. These analyses made use of traditional cut and count methods. While this method has yielded promising results, it has been challenging in the region where the mass difference between SUSY particles is small. Deep learning is currently widely employed in most data analysis tasks, including high energy physics, and has made significant advances in almost all fields for collecting and interpreting huge data samples. In this paper, a fast and time-efficient classification technique is proposed, utilizing machine learning algorithms to distinguish dark matter signal from SM background in compressed mass spectra scenarios at a center-of-mass energy of 14 TeV. A classification model was built in a short amount of time using 2D histograms produced with less amount of data, effectively reducing computational costs through the transfer learning of pre-trained deep models while maintaining a high level of classification accuracy.