Optical identification of supernova remnants in galaxy NGC 6946 by RTT150 and a general classification of ionized regions by machine learning

Supernova remnants (SNRs), formed from the expanding shells of gas and dust ejected during supernova explosions, are significant for their roles in the chemical evolution of galaxies, and enrichment of the interstellar medium with metals. These objects are crucial to identify, and one common method of distinguishing shocked nebulae, or SNRs, from HII regions ionized by the UV light of hot stars, is through line ratios. The thesis is composed of two main parts. The first part focuses on the galaxy NGC 6946 where photometric and spectroscopic measurements were conducted using the RTT150 telescope. These observations were analyzed using the selection criterion leading to the identification of 71 SNR candidates , including three newly discovered ones. Verification of four SNRs through their spectra is also demonstrated. In the second part, the thesis encompasses the diagnostic tools to differentiate between ionized regions by training machine learning algorithms with synthetic data using of eight emission line ratios. Models that best distinguish SNRs from HII regions were discussed, and it was observed that the OI/Hα ratio performs better than existing methods. The next step included the inclusion of the Planetary Nebula (PNe), and the discussion of models that best seperate there three regions. The effectiveness of the [OII]/[OIII] and [SII]6716/[SII]6731 ratios in this distinction was highlighted.