Abstract

Poster - Splinter JungeAG

Tuesday, 10 September 2024, 16:28   (S25)

Machine learning analysis of supernova remnant simulations

P. Smirnova, E. I. Makarenko, S. D. Clarke, E. Glukhov, S. Walch, I. Vaezzadeh, D. Seifried
I. Physikalisches Institut, Universitat zu Koln, Zulpicher Str. 77, D-50937 Koln, Germany,Institute of Astronomy and Astrophysics, Academia Sinica, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan,Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA

About 15%-60% of all supernova remnants are estimated to interact with dense molecular clouds. In these high density environments, radiative losses are significant. The cooling radiation can be observed in forbidden lines at optical wavelengths. In this work, we aim to determine whether supernovae at different positions within a molecular cloud (with/without magnetic fields) can be distinguished based on their optical emission, e.g. Hα (λ 6563), Hβ (λ 4861), [O iii] (λ 5007), [S ii] (λ 6717, 6731), [N ii] (λ 6583)), using machine learning (e.g. principle component analysis and k-means clustering). Methods. We have conducted a statistical analysis of the optical line emission of simulated supernovae interacting with molecular clouds that formed from the multi-phase interstellar medium modelled in the SILCC-Zoom simulations with and without magnetic field. This work is based on post-processing of simulations which have been carried out with the 3-D (magneto)hydrodynamic code FLASH. Our data set consists of 22 simulations. The supernovae are placed at a distance of either 25 pc or 50 pc from the molecular cloud centre of mass, respectively. First, we calculate optical synthetic emission maps (taking into account dust attenuation within the simulation sub-cube) with a post-processing code based on MAPPINGS V cooling tables. Second, we analyse the data set of synthetic observations using principle component analysis to identify clusters with the k-means algorithm. In addition, we make use of BPT diagrams as a diagnostic of shock-dominated regions. We find that the presence or absence of magnetic fields has no statistically significant effect on the optical line emission. However, the ambient density distribution at the site of the supernova changes the entire evolution and morphology of the supernova remnant. Due to the higher density environment when the supernova goes of at smaller distances from the clouds center, it is possible to differentiate between 25 pc and 50 pc distances in a statistically significant manner.