Abstract
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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References
A.E. Szymkowiak, R.L. Kelley, S.H. Moseley, C.K. Stahle, J. Low Temp. Phys. 93(3), 281–285 (1993)
E.R. Malinowski, Factor analysis in chemistry, 3rd edn. (John H. Wiley & Sons, New York, 2002)
S. E. Busch, J. S. Adams, S. R. Bandler, J. A. Chervenak, M. E. Eckart, F. M. Finkbeiner, D. J. Fixsen, R. L. Kelley, C. A. Kilbourne, J. P. Porst, F. S. Porter, J. E. Sadleir, and S. J. Smith. J. Low Temp. Phys. This Special Issue (2015)
M. Lerotic, C. Jacobsen, T. Schäfer, S. Vogt, Ultramicroscopy 100(1–2), 35–57 (2004)
M. Lerotic, R. Mak, S. Wirick, F. Meirer, C. Jacobsen, J. Synchrotron Rad. 21, 1–7 (2014)
We note that our covariance-based approach makes no reference to the spectral characteristics of the noise; we only ask if it is correlated with the signal, or not. It may be that further improvements in our analysis could be obtained by separating white noise from non-white noise, as suggested by an anonymous reviewer; we hope to explore this in future work
G. Ulbricht, B.A. Mazin, P. Szypryt, A.B. Walter, C. Bockstiegel, B. Bumble, Appl. Phys. Lett. 106(25), 251103 (2015)
O. Quaranta, T.W. Cecil, L. Gades, B. Mazin, A. Miceli, Supercond. Sci. Technol. 26(10), 105021 (2013)
A. Miceli, T.W. Cecil, L. Gades, O. Quaranta, J. Low Temp. Phys. 176(3–4), 497–503 (2014)
B. Alpert, E. Ferri, D. Bennett, M. Faverzani, J. Fowler, A. Giachero, J. Hays-Wehle, M. Maino, A. Nucciotti, A. Puiu, and J. Ullom. J. Low Temp. Phys. This Special Issue (2015)
Using the maximum pulse height as a proxy for photon interaction position, the data’s new distribution along the y-axis correlates with the photon interaction position, and the projection onto the x-axis (i.e., energy) has a very weak dependence on position
Acknowledgments
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
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DOI: https://doi.org/10.1007/s10909-016-1480-5