Using Machine Learning to Infer the Progenitor Properties of Core Collapse Supernova Remnants

There is a clear connection between the evolutionary properties of a massive star and the properties of the resultant supernova and remnant. Here we present new results where we have modeled the crade-to-grave evolution of massive stars with progenitor masses between 10 and 30 solar masses, out to supernova remnant ages of 5000 years. These models provide clear dynamical and spectral diagnostics which can be compared against observations of Galactic and Magellanic Cloud supernova remnants. In this talk, I will discuss the use of these diagnostics and how they can provide clues to the progenitor’s initial mass and evolution. As supernovae are inherently asymmetric, I will discuss how linear combinations of symmetric models, the so-called radial flow approximation, can be used to approximate an asymmetric explosion. We selected a subset of asymmetric models to train a k-nn algorithm. We then applied these models to Galactic and Magellanic Cloud remnants in order to understand the properties of the progenitor systems and the degree of asymmetry in the explosion. In this talk, I present results from this study, discuss limitations of our approach, and present ideas on how these methods may be extended to use in extragalactic SNR surveys which will be performed with future, proposed high resolution X-ray telescopes.