Learning to Sample and Classify Supernova Remnants from Datasets Collected by Current & Future Gamma-ray Observatories
Abstract: Supernova remnants (SNRs) are key gamma-ray sources to investigate the cosmic ray (CR) acceleration and escape mechanisms. So, it is crucial to study the gamma-ray production models in SNRs and to disentangle the unique interstellar medium that SNRs expand into. However, the number of detected and resolved SNRs has to increase so that we can have a more realistic classification scheme for SNRs and obtain a better theoretical insight into CR phenomena happening within and around SNRs. More powerful gamma-ray observatories, such as Cherenkov Telescope Array, with better angular resolution and higher sensitivity in detecting and resolving gamma-ray sources are being constructed and they will produce big volumes of data. So, we have to come up with effective methods to handle these data by implementing smart algorithms (e.g. machine learning - ML). In the first part of my talk, I will discuss the ML tools that I plan to use for sampling artificial SNRs via a customised Normalising Flow model and using these artificial samples to train a classifier for recognising SNRs in big data sets. In the second part, I will talk about the spectral modelling of these objects, which are classified as SNRs, using multi-wavelength data.