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

D. Yan, T. Cecil, L. Gades, Chris Jacobsen, T. Madden, A. Miceli*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)397-404
Number of pages8
JournalJournal of Low Temperature Physics
Volume184
Issue number1-2
DOIs
StatePublished - Jul 1 2016

Keywords

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

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics

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