Hintze Centre for Astrophysical Surveys

Abstract:

In recent years, the black hole candidate X-ray binary system H1743-322 has undergone outbursts and it has been observed with X-ray and radio telescopes. We present 1.3 GHz MeerKAT radio data from the ThunderKAT Large Survey Project on radio transients for the 2018 outburst of H1743-322. We obtain seven detections from a weekly monitoring programme and use publicly available Swift X-ray Telescope and MAXI data to investigate the radio/X-ray correlation of H1743-322 for this outburst. We compare the 2018 outburst with those reported in the literature for this system and find that the X-ray outburst reported is similar to previously reported 'hard-only' outbursts. As in previous outbursts, H1743-322 follows the 'radio-quiet' correlation in the radio/X-ray plane for black hole X-ray binaries, and the radio spectral index throughout the outburst is consistent with the 'radio-quiet' population.

Abstract:

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.