Rubin-LSST

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.

Abstract:

MAXI J1535-571 is a Galactic black hole candidate X-ray binary that was discovered going into outburst in 2017 September. In this paper, we present comprehensive radio monitoring of this system using the Australia Telescope Compact Array, as well as the MeerKAT radio observatory, showing the evolution of the radio jet during its outburst. Our radio observations show the early rise and subsequent quenching of the compact jet as the outburst brightened and then evolved toward the soft state. We constrain the compact jet quenching factor to be more than 3.5 orders of magnitude. We also detected and tracked (for 303 days) a discrete, relativistically moving jet knot that was launched from the system. From the motion of the apparently superluminal knot, we constrain the jet inclination (at the time of ejection) and speed to ≤45° and ≥0.69 c, respectively. Extrapolating its motion back in time, our results suggest that the jet knot was ejected close in time to the transition from the hard intermediate state to soft intermediate state. The launching event also occurred contemporaneously with a short increase in X-ray count rate, a rapid drop in the strength of the X-ray variability, and a change in the type-C quasi-periodic oscillation (QPO) frequency that occurs >2.5 days before the first appearance of a possible type-B QPO.

Abstract:

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). Correlated double sampling (CDS) is a process used in many charge-coupled device readout systems to cancel the reset noise component that would otherwise dominate. CDS processing typically consists of subtracting the integrated video signal during a "signal" period from that during a "reset" period. The response of this processing depends, therefore, on the shape of the video signal with respect to the integration bounds. In particular, the amount of noise appearing in the final image and the linearity of the pixel value with signal charge are affected by the choice of the CDS timing intervals. We use a digital CDS readout system which highly oversamples the video signal (as compared with the pixel rate) to reconstruct pixel values for different CDS timings using identical raw video signal data. We use this technique to develop insights into optimal strategy for selecting CDS timings both in the digital case (where the raw video signal may be available) and in the general case (where it is not). In particular, we show that the linearity of the CDS operation allows subtraction of the raw video signals of pixels in bias images from those in illuminated images to directly show the effects of CDS processing on the final (subtracted) pixel values.