<scp>CASPER</scp>‐<scp>AI</scp>: Machine Learning Applied to Active Collimation in Monolithic Arrays of <scp>SDDs</scp>

ABSTRACT Monolithic arrays of Silicon Drift Detectors (SDDs) have advanced X‐ray spectroscopy by providing excellent energy resolution and high rates at near ambient temperatures. Despite these benefits, they are affected by charge sharing (CS), where photogenerated charge clouds spread across adjacent channels. This effect causes partial charge collection by neighboring pixels and impacts spectroscopic performance. To address this issue, we developed CASPER‐AI (Charge‐sharing Analysis and Spectroscopic Performance Enhancement via Reconstruction through AI), an innovative approach that leverages Machine Learning (ML) and particularly Decision Trees (DT) for effective CS mitigation. CASPER‐AI combines DT classification with CS event reconstruction, thus enhancing spectral quality and recovering useful signals that would otherwise be discarded. Experimental characterization with a monolithic SDD‐based 16‐channel detection module demonstrated significant improvements in peak‐to‐tail ratio and full‐energy event recovery.