Machine learning approaches for improved scalability of metallic magnetic calorimeters

Metallic magnetic calorimeters are a promising tool for high-precision x-ray spectroscopy. However, the signal processing and calibration pipeline required to exploit their full potential is complex and difficult to scale for large, highly pixelated detector arrays. In this work, we investigate ML (Machine Learning) methods for maXs-series detectors, focusing on pulse classification and artifact rejection as well as pulse-shape analysis and feature extraction. By combining unsupervised label auto-discovery with supervised classification and regression, we obtain robust and scalable artifact rejection for high-confidence template building. For ML-based feature extraction, our results constitute a promising proof of principle: in simulation, the ML estimator remains competitive across realistic pulse-shape and noise variation. On real detector data, the method reaches performance comparable to the hand-optimized finite impulse response baseline before gain-drift correction, while further domain adaptation is required to match the established manual analysis pipeline. Both results represent important steps toward a fully automated and scalable data processing procedure.