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.
- Principal component analysis (PCA)
- Pulse processing
- Shape variance
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Materials Science(all)
- Condensed Matter Physics