Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis

作者：Yan D; Cecil T; Gades L; Jacobsen C; Madden T; Miceli A^*

来源：Journal of Low Temperature Physics, 2016, 184(1-2): 397-404.

DOI：10.1007/s10909-016-1480-5

• Principal component analysis (PCA)
• Pulse processing
• Shape variance
• Microcalorimeter

We present a method using principal component analysis (PCA) to process x-ray pulses with severe shape variation where traditional optimal filter methods fail. We demonstrate that PCA is able to noise-filter and extract energy information from x-ray pulses despite their different shapes. We apply this method to a dataset from an x-ray thermal kinetic inductance detector which has severe pulse shape variation arising from position-dependent absorption.

• 出版日期2016-7
• 单位西北大学