Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis
Instrumentation and Detectors
2016-03-23 v1 Instrumentation and Methods for Astrophysics
Data Analysis, Statistics and Probability
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.
Keywords
Cite
@article{arxiv.1601.01651,
title = {Processing of X-ray Microcalorimeter Data with Pulse Shape Variation using Principal Component Analysis},
author = {Daikang Yan and Thomas Cecil and Lisa Gades and Chris Jacobsen and Timothy Madden and Antonino Miceli},
journal= {arXiv preprint arXiv:1601.01651},
year = {2016}
}
Comments
Accepted for publication in J. Low Temperature Physics, Low Temperature Detectors 16 (LTD-16) conference