Machine Learning Approaches for Improved Scalability of Metallic Magnetic Calorimeters
Metallic Magnetic Calorimeters (MMCs) are a promising new tool for high precision X-ray spectroscopy. However, the complexity of the detector response and the need for scalable processing pipelines pose significant challenges for their widespread adoption. In this work, we explore the application of Machine Learning (ML) methods to address these challenges and enhance the performance of MMCs. We demonstrate how ML can be used for pulse classification and artifact rejection, as well as for pulse shape analysis and feature extraction. By leveraging unsupervised learning techniques for label auto-discovery and supervised learning for classification and regression tasks, we show that ML can provide robust and scalable solutions for MMC signal processing. Our results indicate that ML-based approaches can achieve comparable performance to traditional methods while offering greater adaptability and efficiency, paving the way for the next generation of high-precision X-ray spectroscopy with MMCs.