An end-to-end experimental pipeline for automated track identification for paleo-detectors
Paleo-detectors aim to exploit the damage tracks left in natural minerals by cosmic messengers over geological times to reconstruct past fluxes of energetic particles. For cosmic rays, the expected signal is abundant, and the tracks are micron-scale, so they can be revealed by etching and read out with conventional optical microscopy; what remains challenging is turning a bulk sample into a calibrated track-length spectrum that can be quantitatively confronted with theoretical predictions. In this work we present the experimental pipeline we developed for this purpose within the INFN PRImuS experiment, from sample preparation and automated multi-focus microscopy to track recognition with OptimusPrimus, a two-stage machine-learning algorithm, and to a forward-folding model that propagates geometric sectioning, etching and the measured detection response into simulated spectra. The pipeline was built and validated on chemically etched obsidian where the recovered track-length distribution is statistically compatible with expert annotation. In parallel, we developed and characterized a dry argon-plasma etching protocol for halite, an evaporite mineral of particular interest as a low-background cosmic-ray paleo-detector. Since every stage of the pipeline is mineral-agnostic, with only the trained networks and the detection response to be re-derived from annotated data, we are now applying it to plasma-etched halite with the aim of identifying cosmic-ray-induced tracks.