Kinematics of Supernova Remnants Using Multiepoch Maximum Likelihood Estimation: Chandra Observation of Cassiopeia A as an Example

TL;DR

This paper introduces a multiepoch maximum likelihood estimation method incorporating kinematic features and PSF effects to accurately analyze supernova remnant dynamics, demonstrated on Cassiopeia A with Chandra data.

Contribution

The paper presents a novel multiepoch MLE framework that integrates kinematic features and machine learning to analyze SNR dynamics with high accuracy.

Findings

Accurate estimates of shock and faint features motions in Cassiopeia A.

Extension of MLE to classify asymmetric structures using k-means.

Identification of a component interacting with circumstellar material.

Abstract

Decadal changes in a nearby supernova remnant (SNR) were analyzed using a multiepoch maximum likelihood estimation (MLE) approach. To achieve greater accuracy in capturing the dynamics of SNRs, kinematic features and point-spread function effects were integrated into the MLE framework. Using Cassiopeia A as a representative example, data obtained by the Chandra X-ray Observatory in 2000, 2009, and 2019 were utilized. The proposed multiepoch MLE was qualitatively and quantitatively demonstrated to provide accurate estimates of various motions, including shock waves and faint features, across all regions. To investigate asymmetric structures, such as singular components that deviate from the direction of expansion, the MLE method was extended to combine multiple computational domains and classify kinematic properties using the $k$-means algorithm. This approach allowed for the mapping of different physical states onto the image, and one classified component was suggested to interact with circumstellar material by comparison with infrared observations from the James Webb Space Telescope. Thus, this technique will help quantify the dynamics of SNRs and discover their unique evolution.