Wulf Dallas, Jaeckel Felix, McCammon Dan, Chervenak James A, Eckart Megan E
University of Wisconsin-Madison, 1150 University Avenue, Madison, WI 53706, USA.
NASA/Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA.
J Appl Phys. 2020 Nov 7;128(17). doi: 10.1063/5.0026193. Epub 2020 Nov 5.
Here we present a general algorithm for processing microcalorimeter data with special applicability to data with high photon count rates. Conventional optimal filtering, which has become ubiquitous in microcalorimeter data processing, suffers from its inability to recover overlapped pulses without sacrificing spectral resolution. The technique presented here was developed to address this particular shortcoming, and does so without imposing any assumptions beyond those made by the conventional technique. We demonstrate the algorithm's performance with a data set that approximately satisfies these assumptions, and which is representative of a wide range of microcalorimeter applications. We also apply the technique to a highly non-linear data set, examining the impact on performance in the limit that these assumptions break down.
在此，我们提出一种处理微热量计数据的通用算法，该算法特别适用于具有高光子计数率的数据。传统的最优滤波在微热量计数据处理中已无处不在，但其缺点是在不牺牲光谱分辨率的情况下无法恢复重叠脉冲。此处提出的技术旨在解决这一特殊缺点，并且在不施加任何超出传统技术所做假设的前提下做到了这一点。我们用一个大致满足这些假设且代表广泛微热量计应用的数据集来演示该算法的性能。我们还将该技术应用于一个高度非线性的数据集，研究在这些假设不成立的极限情况下对性能的影响。
