Model-Independent Machine Learning Approach for Nanometric Axial Localization and Tracking

Recent advancements in machine learning have significantly enhanced the precision and efficiency of data-driven methodologies in scientific applications. These methods have found applications in a variety of fields, including physics, medicine, and space sciences, where they help addressing complex challenges which require high-precision measurements. One such application is directional dark matter search experiments that require precise measurements of ions recoiling after their interactions with dark matter particles [1, 2]. Due to their extremely low kinetic energies, in the 1 100 keV range, recoiling ions produce tracks ranging from a few millimeters in gases at low pressure to a few hundreds of nanometers in solids [2, 3]. Taking into account that the required detector mass in practice amounts to several tons, the choice of solid materials as a sensitive medium is advantageous.