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Abstract:

We present a convolutional neural network (CNN) for use in the real–bogus classification of transient detections made by the Asteroid Terrestrial-impact Last Alert System (ATLAS) and subsequent efforts to improve performance since initial development. In transient detection surveys, the number of alerts made outstrips the capacity for human scanning, necessitating the use of machine learning aids to reduce the number of false positives presented to annotators. We take a sample of recently annotated data from each of the three operating ATLAS telescope with 340 000 real (known transients) and 1030 000 bogus detections per model. We retrained the CNN architecture with these data specific to each ATLAS unit, achieving a median false positive rate (FPR) of 0.72 per cent for a 1.00 per cent missed detection rate. Further investigations indicate that if we reduce the input image size it results in increased FPR. Finally architecture adjustments and comparisons to contemporary CNNs indicate that our retrained classifier is providing an optimal FPR. We conclude that the periodic retraining and readjustment of classification models on survey data can yield significant improvements as data drift arising from changes in the optical and detector performance can lead to new features in the model and subsequent deteriorations in performance.

Abstract:

Lasair is the UK Community Broker for transient alerts from the Legacy Survey of Space and Time from the Vera C. Rubin Observatory. We explain the system’s capabilities, how users can achieve their scientific goals, and how Lasair is implemented. Lasair offers users a kit of parts that they can use to build filters to concentrate their desired alerts. The kit has novel light-curve features, sky context, watchlists of special sky objects and regions of the sky, dynamic cross-matching with catalogues of known astronomical sources, and classifications and annotations from other users and partner projects. These resources can be shared with other users, copied, and modified. Lasair offers real-time machine-to-machine notifications of filtered transient alerts. Even though the Rubin Observatory is not yet complete, Lasair is a mature system: it has been processing and serving data from the similarly formatted stream of the Zwicky Transient Facility alerts.

Abstract:

We use data from the MeerTime project on the MeerKAT telescope to ask whether the radio emission properties of millisecond pulsars (MSPs) and slowly rotating, younger pulsars (SPs) are similar or different. We show that the flux density spectra of both populations are similarly steep, and the widths of MSP profiles obey the same dependence on the rotational period as slow pulsars. We also show that the polarization of MSPs has similar properties to slow pulsars. The commonly used pseudo-luminosity of pulsars, defined as the product of the flux density and the distance squared, is not appropriate for drawing conclusions about the relative intrinsic radio luminosity of SPs and MSPs. We show that it is possible to scale the pseudo-luminosity to account for the pulse duty cycle and the solid angle of the radio beam, in such a way that MSPs and SPs do not show clear differences in intrinsic luminosity. The data therefore support common emission physics between the two populations in spite of orders of magnitude difference in their period derivatives and inferred, surface, dipole magnetic field strengths.