Prof Chris Lintott

Abstract:

Automated planetary transit detection has become vital to identify and prioritize candidates for expert analysis and verification given the scale of modern telescopic surveys. Current methods for short-period exoplanet detection work effectively due to periodicity in the transit signals, but a robust approach for detecting single-transit events is lacking. However, volunteer-labeled transits collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find that this model recovers planet candidates (TESS objects of interest; TOIs) at a precision and recall rate exceeding those of volunteers, with a 20% improvement in the area under the precision-recall curve and 10% more TOIs identified in the top 500 predictions on average per sector. Importantly, the model also recovers almost all planet candidates found by volunteers but missed by current automated methods (PHT community TOIs). Finally we retrospectively utilise the model to simulate live deployment in PHT to reprioritize candidates for analysis. We also find that multiple promising planet candidates, originally missed by PHT, would have been found using our approach, showing promise for upcoming real-world deployment.

Abstract:

<jats:title>ABSTRACT</jats:title> <jats:p>In this work, we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters (‘feature sets’) as applied to two anomaly detection techniques in the astronomaly package and analyse our performance by comparison with citizen science labels on the same data set. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best-performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other data sets, and as a real-time transient detection system for upcoming large surveys.</jats:p>

Abstract:

Active galactic nucleus (AGN) growth in disc-dominated, merger-free galaxies is poorly understood, largely due to the difficulty in disentangling the AGN emission from that of the host galaxy. By carefully separating this emission, we examine the differences between AGNs in galaxies hosting a (possibly) merger-grown, classical bulge, and AGNs in secularly grown, truly bulgeless disc galaxies. We use galfit to obtain robust, accurate morphologies of 100 disc-dominated galaxies imaged with the Hubble Space Telescope. Adopting an inclusive definition of classical bulges, we detect a classical bulge component in per cent of the galaxies. These bulges were not visible in Sloan Digital Sky Survey photometry, however these galaxies are still unambiguously disc-dominated, with an average bulge-to-total luminosity ratio of . We find some correlation between bulge mass and black hole mass for disc-dominated galaxies, though this correlation is significantly weaker in comparison to the relation for bulge-dominated or elliptical galaxies. Furthermore, a significant fraction ( per cent) of our black holes are overly massive when compared to the relationship for elliptical galaxies. We find a weak correlation between total stellar mass and black hole mass for the disc-dominated galaxies, hinting that the stochasticity of black hole–galaxy co-evolution may be higher in disc-dominated than bulge-dominated systems.