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.
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Use of the Center for Nanoscale Materials was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. Work at Argonne National Laboratory was supported by the U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. Devices in this paper were fabricated at CNM; we gratefully acknowledge assistance from Ralu Divan, Leo Ocola, Dave Czaplewski, and Suzanne Miller at CNM. We thank Mirna Lerotic and Rachel Mak for useful discussion on PCA. Finally, we thank the reviewers for their useful insights.
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Yan, D., Cecil, T., Gades, L. et al. Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis. J Low Temp Phys 184, 397–404 (2016). https://doi.org/10.1007/s10909-016-1480-5
- Principal component analysis (PCA)
- Pulse processing
- Shape variance