2015/11/12 by J. W. Fowler, Joseph W. Fowler, Bradley K. Alpert +10 · 2 citations
Physics and Astronomy · #Artificial intelligence #Computational physics #Computer science #Detector #Energy (signal processing) #Optics #Physics #Physics of Superconductivity and Magnetism #Pipeline (software) #Pulse (music) #Quantum mechanics #Resolution (logic) #Spectrometer #Superconducting and THz Device Technology #Superconductivity in MgB2 and Alloys #physics.ins-det
paper · pdf · doi:10.1007/s10909-015-1380-0
published as J. Low Temperature Phys., Vol 184 (2016), pp 374-381 · Accepted for publication in J. Low Temperature Physics, special issue for the proceedings of the Low Temperature Detectors 16 conference
arxiv created 2015/11/12 · openalex publication_date 2015/12/09 · arxiv updated 2017/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The analysis of data from x-ray microcalorimeters requires great care; their excellent intrinsic energy resolution cannot usually be achieved in practice without a statistically near-optimal pulse analysis and corrections for important systematic errors. We describe the essential parts of a pulse-analysis pipeline for data from x-ray microcalorimeters, including steps taken to reduce systematic gain variation and the unwelcome dependence of filtered pulse heights on the exact pulse-arrival time. We find these steps collectively to be essential tools for getting the best results from a microcalorimeter-based x-ray spectrometer.