Probing Plasma Properties in Supernova Remnants observed by XMM-Newton

We propose to produce a unique database of plasma conditions for a sample of supernovaremnants (SNRs) observed with XMM-Newton. The bulk of the material in supernova remnants is ejecta, circumstellar matter, or interstellar plasma heated by fast-moving shocks and emitting strongly at X-ray wavelengths. Careful characterization of the plasma abundances, ionization ages, andtemperatures can place constraints on supernova and stellar evolution models. We will use archival X-ray observations of a wide variety of SNRs to produce distributions of temperature, ionization state, and abundances of plasma throughout the entire volume of each SNR. We will accomplish this by applying a new technique, Smoothed Particle Inference (SPI), to the XMM-Newton observations of SNRs. SPI is a Bayesian modeling process that fits a population of gas blobs (“smoothed particles”) such that their superposed emission reproduces the observed spatial and spectral distribution of photons. Emission-weighted distributions of plasma properties are then extracted from the properties of the individual blobs. This technique has important advantages over the analysis techniques applied in the past, which implicitly assumed that remnants are two-dimensional objects in which each line of sight encompasses a single plasma. In contrast, SPI allows superposition of as many blobs of plasma as are needed to match the spectrum observed in each direction, without the need to bin the data spatially. In addition to providing deeper insight into the conditions of individual remnants and their relation to the surrounding medium and progenitor stars, application of SPI to a larger sample of SNRs allows for consistent comparisons of plasma conditions between remnants. The supernova remnants in the proposed sample include both core-collapse and Type Ia, and cover a wide range of ages and environments. This will enable us to investigate relationships between abundance, temperature, and ionization age distributions and SNR type, environment, and age, ultimately providing a more complete understanding of supernova remnants as a class. Much of the initial SPI analysis is already complete. At Northwestern University, Dr. Frank will continue the SPI analysis, while focusing on the next step, which is in-depth analysis of the SPI results from the first 9 SNRs. This involves constructing the distributions and maps of plasma properties, and comparing these across the entire sample and with evolutionary SNR models.