Smoothed Particle Inference Analysis of SNR DEM L71

Kari Ann Frank, Vikram Dwarkadas, Aldo Panfichi, Ryan Matthew Crum, David N. Burrows

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Supernova remnants (SNRs) are complex, three-dimensional objects; properly accounting for this complexity when modeling the resulting X-ray emission presents quite a challenge and makes it difficult to accurately characterize the properties of the full SNR volume. We apply for the first time a novel analysis method called smoothed particle inference, which can be used to study and characterize the structure, dynamics, morphology, and abundances of the entire remnant with a single analysis. We apply the method to the SNe Ia remnant DEM L71. We present histograms and maps showing global properties of the remnant, including temperature, abundances of various elements, abundance ratios, and ionization age. Our analysis confirms the high abundance of Fe within the ejecta of the supernova, which has led to it being typed as a Ia. We demonstrate that the results obtained with this method are consistent with results derived from numerical simulations carried out by us, as well as with previous analyses in the literature. At the same time, we show that despite its regular appearance, the temperature and other parameter maps exhibit highly irregular substructure that is not captured with typical X-ray analysis methods.

Original languageEnglish (US)
Article number14
JournalAstrophysical Journal
Volume875
Issue number1
DOIs
StatePublished - Apr 10 2019

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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