Description
In this talk I will present an analysis of predicted radio galaxy morphologies for the Radio Galaxy Zoo (RGZ) catalogue, classified using a pre-trained radio galaxy foundation model designed with a view to the analysis of future SKA data. Our study examines over 14,000 radio galaxies, and provides predicted classifications for approximately 5,900 FRIs and 8,100 FRIIs. We investigate the impacts of both pre-training and fine-tuning data selection on model performance for the downstream classification task, and show that while different pre-training data choices affect model confidence they do not appear to cause systematic generalisation biases for the range of physical and observation characteristics considered in this work; however, we note that the same is not necessarily true for fine-tuning. As seen in previous studies, our results show overlap between morphologically classified FRI and FRII luminosity-size distributions and we find that the model's confidence in its predictions is lowest in this overlap region, suggesting that source morphologies are more ambiguous. We identify the presence of low-luminosity FRII sources, the proportion of which is consistent with previous studies. However, a comparison of the low-luminosity FRII sources found in this work with those identified by previous studies reveals differences that may indicate their selection is influenced by the choice of classification methodology. As automated approaches to astronomical source identification and classification become increasingly prevalent, we highlight training data choices that can affect the model outputs and propagate into downstream analyses.
