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Supernova remnants (SNRs) are complex, three-dimensional objects; properlyaccounting for this complexity when modeling the resulting X-ray emissionpresents quite a challenge and makes it difficult to accurately characterizethe properties of the full SNR volume. We apply for the first time a novelanalysis method, Smoothed Particle Inference, that can be used to study andcharacterize the structure, dynamics, morphology, and abundances of the entireremnant with a single analysis. We apply the method to the Type Ia supernovaremnant DEM L71. We present histograms and maps showing global properties ofthe remnant, including temperature, abundances of various elements, abundanceratios, and ionization age. Our analysis confirms the high abundance of Fewithin the ejecta of the supernova, which has led to it being typed as a Ia. Wedemonstrate that the results obtained via this method are consistent withresults derived from numerical simulations carried out by us, as well as withprevious analyses in the literature. At the same time, we show that despite itsregular appearance, the temperature and other parameter maps exhibit highlyirregular substructure which is not captured with typical X-ray analysismethods.

Smoothed Particle Inference Analysis of SNR DEM L71