Machine learning methods are explored for pulse classification, artifact rejection, and shape analysis in metallic magnetic calorimeters to improve scalability over traditional signal processing.
Enss ,\ 10.1007/b12169 title Cryogenic Particle Detection ,\ Topics in Applied Physics \ ( publisher Springer-Verlag ,\ address Berlin Heidelberg ,\ year 2005 ) NoStop
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Machine Learning Approaches for Improved Scalability of Metallic Magnetic Calorimeters
Machine learning methods are explored for pulse classification, artifact rejection, and shape analysis in metallic magnetic calorimeters to improve scalability over traditional signal processing.