Speaker
Kaustuv Basu
(Uni Bonn)
Description
Determination of galaxy cluster masses from astronomical data is one of the primary prerequisites for their cosmological analysis and various deep learning methods have been tested with simulated data sets. Although the attention of the community is moving towards more advanced architectures like GANs and vision transformers, improvements can still be made with the traditional convolutional networks with simple physical inputs. These range from very simple exploitation of spherical symmetry of the objects, to training with polarization data in addition to the total intensity images. Feedback is welcome on whether there are some fundamental limitations for these convolutional networks and whether one should adopt newer models.
Primary author
Kaustuv Basu
(Uni Bonn)
