Breakthrough Listen

Abstract:

MERLIN observations of the unusually slow nova V723 Cas are presented. Nine epochs of 6-cm data between 1996 and 2001 are mapped, showing the initial expansion and brightening of the radio remnant, the development of structure and the final decline. A radio light curve is presented and fitted by the standard Hubble flow model for radio emission from novae in order to determine the values of various physical parameters for the shell. The model is consistent with the overall development of the radio emission. Assuming a distance of 2.39 (+/-0.38) kpc and a shell temperature of 17000 K, the model yields values for expansion velocity of 414 +/- 0.1 km s^-1 and shell mass of 1.13 +/- 0.04 * 10^-4 Msolar. These values are consistent with those derived from other observations although the ejected masses are rather higher than theoretical predictions. The structure of the shell is resolved by MERLIN and shows that the assumption of spherical symmetry in the standard model is unlikely to be correct.

Abstract:

We compare the predictions of simple models for the radio emission from classical novae with the MERLIN radio observations of nova V723 Cas (Nova Cas 1995). Spherically symmetric and ellipsoidal radiative transfer models are implemented in order to generate synthetic emission maps. These are then convolved with an accurate representation of the uv coverage of MERLIN. The parameters and geometry of the shell model are based on those returned by fitting models to the observed light curve. This allows direct comparison of the model images with the nine 6-cm MERLIN images of V723 Cas. It is found that the seemingly complex structure (clumping, apparent rotation) evident in the observations can actually be reproduced with a simple spherical emission model. The simulations show that a 24-h track greatly reduces the instrumental effects and the synthetic radio map is a closer representation of the true (model) sky brightness distribution. It is clear that interferometric arrays with sparse uv coverage (e.g. MERLIN, VLBA) will be more prone to these instrumental effects especially when imaging ring-like objects with time-dependent structure variations. A modelling approach such as that adopted here is essential when interpreting observations. © 2007 RAS.

Abstract:

Modern radio telescopes are data-intensive machines, producing many TB of data every night. Amongst this deluge of data are transient and variable phenomena, whose study can shed new light on processes as varied as stellar dynamos and the accretion discs in supermassive black holes. In this work I demonstrate the applicability of different methods to the discovery of these astrophysical transients and variables coming from telescopes such as MeerKAT.

I first consider a standard approach to discovering transients by characterising their variability. By making use of even modest sampling with the high sensitivity and wide field of view of MeerKAT, I demonstrate how we are now able to uncover new transients almost by accident - if we exclude the vast amount of time spent planning, building and operating excellent telescopes, efficient pipelines and well- crafted observing proposals. In this work I found a stellar flare from a nearby M dwarf, which was then followed up and complemented by optical and X-ray photometry and spectroscopy, providing new insights on the system.

Next I built a citizen science platform in order to perform such transient searches at scale, making use of a wide range of data available in the MeerKAT archive. I detail the process of review and beta-testing that resulted in the final design of the Bursts from Space: MeerKAT project. Over 1000 volunteers took part, demonstrating a healthy appetite for further Zooniverse data releases. Volunteers discovered or recovered a wide range of phenomena, from flare stars and pulsars to scintillating AGN and transient OH maser emission. I was also able to use the known transients in our fields to understand some reasons why interesting sources may be missed and will fold this learning through to future iterations of the project. This is the first demonstration of volunteers finding radio transients in images.

Finally, I show how anomaly detection, an unsupervised machine learning approach, is a suitable tool for finding these variable phenomena at scale, as is required for modern astronomical surveys. I use three feature sets as applied to two anomaly detection techniques in the Astronomaly package and analyse anomaly detection performance by comparison with citizen science labels. By using transients found by citizen scientists as a ground truth I demonstrate that anomaly detection techniques can recall over half of the radio transients within 10% of the sample dataset. I find that the choice of feature set is crucial, especially when considering available resources for human inspection and follow-up. I find that active learning on ∼2% of the data improves recall by up to 10%, depending on the feature-model pair. The best performing feature-model pairs result in a factor of 5 times fewer sources requiring vetting by humans. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other datasets, a real-time transient detection system and upcoming large surveys.