Using the X-ray Morphologies of Young Supernova Remnants to Constrain Explosion Type, Ejecta Distribution, and Chemical Mixing
Laura A. Lopez (UCSC), Enrico Ramirez-Ruiz (UCSC), Daniela, Huppenkothen (Amsterdam), Carles Badenes (Tel-Aviv, Weizmann Institute of, Science), David Pooley (Eureka)

TL;DR
This study uses statistical analysis of X-ray images to distinguish supernova remnant types and understand ejecta mixing, revealing that morphology can classify explosion type but not ejecta distribution.
Contribution
Introduces a novel morphological classification method for SNRs using power-ratio and wavelet analysis, enabling explosion type identification from X-ray emission morphology.
Findings
Type Ia SNRs are more spherical and symmetric than core-collapse SNRs.
Morphology can classify SNR explosion type even with weak or low-resolution spectra.
Ejecta mixing is efficient in both Type Ia and core-collapse SNRs, with W49B as an exception.
Abstract
Supernova remnants (SNRs) are a complex class of sources, and their heterogeneous nature has hindered the characterization of their general observational properties. To overcome this challenge, we use statistical tools to analyze the Chandra X-ray images of Galactic and Large Magellanic Cloud SNRs. We apply two techniques, a power-ratio method (a multipole expansion) and wavelet-transform analysis, to measure the global and local morphological properties of the X-ray line and thermal emission in twenty-four SNRs. We find that Type Ia SNRs have statistically more spherical and mirror symmetric thermal X-ray emission than core-collapse (CC) SNRs. The ability to type SNRs based on thermal emission morphology alone enables, for the first time, the typing of SNRs with weak X-ray lines or with low-resolution spectra. We identify one source, SNR G344.7-0.1, as originating from a CC explosion…
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