Application of machine learning techniques to images collected with Charge Coupled Devices to search for Dark Matter

Abstract

ABSTRACT: The Standard Model of particle physics, while being able to make accurate predictions, has been proved to fail to explain various phenomena, such as astronomical dark matter observations. In this work, a machine learning application has been implemented with the goal of studying dark matter candidates. Images from Charge Coupled Devices (CCDs) in different experiments DAMIC/DAMIC-M located underground will be used to test different deep learning algorithms. A U-Net model has been trained with Python’s open-source library Keras. The model performs multi-class image segmentation in order to detect dark matter particle signals among background noise.