The Square Kilometre Array (SKA)

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.

Abstract:

Anomalous Microwave Emission (AME) is a significant component of Galactic diffuse emission in the frequency range $10$-$60\,$GHz and a new window into the properties of sub-nanometre-sized grains in the interstellar medium. We investigate the morphology of AME in the $\approx10^{\circ}$ diameter $\lambda$ Orionis ring by combining intensity data from the QUIJOTE experiment at $11$, $13$, $17$ and $19\,$GHz and the C-Band All Sky Survey (C-BASS) at $4.76\,$GHz, together with 19 ancillary datasets between $1.42$ and $3000\,$GHz. Maps of physical parameters at $1^{\circ}$ resolution are produced through Markov Chain Monte Carlo (MCMC) fits of spectral energy distributions (SEDs), approximating the AME component with a log-normal distribution. AME is detected in excess of $20\,\sigma$ at degree-scales around the entirety of the ring along photodissociation regions (PDRs), with three primary bright regions containing dark clouds. A radial decrease is observed in the AME peak frequency from $\approx35\,$GHz near the free-free region to $\approx21\,$GHz in the outer regions of the ring, which is the first detection of AME spectral variations across a single region. A strong correlation between AME peak frequency, emission measure and dust temperature is an indication for the dependence of the AME peak frequency on the local radiation field. The AME amplitude normalised by the optical depth is also strongly correlated with the radiation field, giving an overall picture consistent with spinning dust where the local radiation field plays a key role.

Abstract:

Cosmic filaments are the channel through which galaxy groups assemble their mass. Cosmic connectivity, namely the number of filaments connected to a given group, is therefore expected to be an important ingredient in shaping group properties. The local connectivity is measured in COSMOS around X-Ray detected groups between redshift 0.5 and 1.2. To this end, large-scale filaments are extracted using the accurate photometric redshifts of the COSMOS2015 catalogue in two-dimensional slices of thickness 120 comoving Mpc centred on the group's redshift. The link between connectivity, group mass and the properties of the brightest group galaxy (BGG) is investigated. The same measurement is carried out on mocks extracted from the lightcone of the hydrodynamical simulation Horizon-AGN in order to control systematics. More massive groups are on average more connected. At fixed group mass in low-mass groups, BGG mass is slightly enhanced at high connectivity, while in high mass groups BGG mass is lower at higher connectivity. Groups with a star-forming BGG have on average a lower connectivity at given mass. From the analysis of the Horizon-AGN simulation, we postulate that different connectivities trace different paths of group mass assembly: at high group mass, groups with higher connectivity are more likely to have grown through a recent major merger, which might be in turn the reason for the quenching of the BGG. Future large-field photometric surveys, such as Euclid and LSST, will be able to confirm and extend these results by probing a wider mass range and a larger variety of environment.

Abstract:

U-band observations with the LSST have yet to be fully optimized in cadence. The straw man survey design is a simple coverage of the medium-deep-fast survey. Here we argue that deep coverage of the four deep drilling fields (XMM-LSS, ECDFS, ELAIS-S1 and COSMOS) has a much higher scientific return, given that these are also the target of the Southern Hemisphere's Square Kilometer Array Pathfinder, the MeerKAT specifically, deep radio observations.