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

TL;DR

This paper introduces a PCA-based method to process x-ray microcalorimeter data with high pulse shape variability, effectively filtering noise and extracting energy information where traditional methods fail.

Contribution

The paper presents a novel application of PCA for processing x-ray pulse data with severe shape variation, improving energy extraction in challenging conditions.

Findings

PCA effectively filters noise in variable pulse shapes.

The method accurately extracts energy information from diverse pulse shapes.

Applicable to detectors with position-dependent absorption effects.

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.