Optimal Probabilistic Catalogue Matching for Radio Sources

Cross-matching catalogues from radio surveys to catalogues of sources at other wavelengths is extremely hard, because radio sources are often extended, often consist of several spatially separated components, and often no radio component is coincident with the optical/infrared host galaxy. Traditionally, the cross-matching is done by eye, but this does not scale to the millions of radio sources expected from the next generation of radio surveys. We present an innovative automated procedure, using Bayesian hypothesis testing, that models trial radio-source morphologies with putative positions of the host galaxy. This new algorithm differs from an earlier version by allowing more complex radio source morphologies, and performing a simultaneous fit over a large field. We show that this technique performs well in an unsupervised mode.Comment: 9 pages, 7 figure

A Molecular Einstein Ring at z=4.12: Imaging the Dynamics of a Quasar Host Galaxy Through a Cosmic Lens

We present high-resolution (0.3") Very Large Array (VLA) imaging of the molecular gas in the host galaxy of the high redshift quasar PSS J2322+1944 (z=4.12). These observations confirm that the molecular gas (CO) in the host galaxy of this quasar is lensed into a full Einstein ring, and reveal the internal dynamics of the molecular gas in this system. The ring has a diameter of ~1.5", and thus is sampled over ~20 resolution elements by our observations. Through a model-based lens inversion, we recover the velocity gradient of the molecular reservoir in the quasar host galaxy of PSS J2322+1944. The Einstein ring lens configuration enables us to zoom in on the emission and to resolve scales down to ~1 kpc. From the model-reconstructed source, we find that the molecular gas is distributed on a scale of 5 kpc, and has a total mass of M(H2)=1.7 x 10^10 M_sun. A basic estimate of the dynamical mass gives M_dyn = 4.4 x 10^10 (sin i)^-2 M_sun, that is, only ~2.5 times the molecular gas mass, and ~30 times the black hole mass (assuming that the dynamical structure is highly inclined). The lens configuration also allows us to tie the optical emission to the molecular gas emission, which suggests that the active galactic nucleus (AGN) does reside within, but not close to the center of the molecular reservoir. Together with the (at least partially) disturbed structure of the CO, this suggests that the system is interacting. Such an interaction, possibly caused by a major `wet' merger, may be responsible for both feeding the quasar and fueling the massive starburst of 680 M_sun/yr in this system, in agreement with recently suggested scenarios of quasar activity and galaxy assembly in the early universe.Comment: 9 pages, 7 figures, to appear in ApJ (accepted June 27, 2008

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Radio source-component association for the LOFAR Two-metre Sky Survey with region-based convolutional neural networks

Large scale structure and cosmolog

Multifrequency VLBA Monitoring of 3C 273 during the INTEGRAL Campaign in 2003 - I. Kinematics of the Parsec Scale Jet from 43 GHz Data

In this first of a series of papers describing polarimetric multifrequency Very Long Baseline Array (VLBA) monitoring of 3C 273 during a simultaneous campaign with the INTEGRAL gamma-ray satellite in 2003, we present 5 Stokes I images and source models at 7 mm. We show that a part of the inner jet (1-2 milliarcseconds from the core) is resolved in a direction transverse to the flow, and we analyse the kinematics of the jet within the first 10 mas. Based on the VLBA data and simultaneous single-dish flux density monitoring, we determine an accurate value for the Doppler factor of the parsec scale jet, and using this value with observed proper motions, we calculate the Lorentz factors and the viewing angles for the emission components in the jet. Our data indicates a significant velocity gradient across the jet with the components travelling near the southern edge being faster than the components with more northern path. We discuss our observations in the light of jet precession model and growing plasma instabilities.Comment: Accepted for publication in Astronomy & Astrophysics, 16 pages, 15 figure

Finding AGN remnant candidates based on radio morphology with machine learning

Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified in radio sky surveys based on their spectral index, or, complementary, through visual inspection based on their radio morphology. However, this is extremely time-consuming when applied to the new large and sensitive radio surveys. Here we aim to reduce the amount of visual inspection required to find AGN remnants based on their morphology, through supervised machine learning trained on an existing sample of remnant candidates. For a dataset of 4107 radio sources, with angular sizes larger than 60 arcsec, from the LOw Frequency ARray (LOFAR) Two-Metre Sky Survey second data release (LoTSS-DR2), we started with 151 radio sources that were visually classified as 'AGN remnant candidate'. We derived a wide range of morphological features for all radio sources from their corresponding Stokes-I images: from simple source catalogue-derived properties, to clustered Haralick-features, and self-organising map (SOM) derived morphological features. We trained a random forest classifier to separate the 'AGN remnant candidates' from the not yet inspected sources. The SOM-derived features and the total to peak flux ratio of a source are shown to be most salient to the classifier. We estimate that $31\pm5\%$ of sources with positive predictions from our classifier will be labelled 'AGN remnant candidates' upon visual inspection, while we estimate the upper bound of the $95\%$ confidence interval for 'AGN remnant candidates' in the negative predictions at $8\%$. Visual inspection of just the positive predictions reduces the number of radio sources requiring visual inspection by $73\%$.Comment: 23 pages; accepted for publication in A&

Learning to Identify Extragalactic Radio Sources

Radio observations of actively accreting supermassive black holes outside of the galaxy can provide insight into the history of galaxies and their evolution. With the construction of fast new radio telescopes and the undertaking of large new radio surveys in the lead-up to the Square Kilometre Array (SKA), radio astronomy faces a `data deluge' where traditional methods of data analysis cannot keep up with the scale of the data. Astronomers are increasingly looking to machine learning to provide ways of handling large-scale data like these. This thesis introduces machine learning methods for use in wide-area radio surveys and demonstrates their application to radio astronomy data. To help understand the issues facing large-scale wide-area radio surveys, and contribute toward their solutions, we consider the problems of automated radio-infrared cross-identification and Faraday complexity classification. We developed an automated machine learning method for cross-identifying radio objects with their infrared counterparts, training the algorithm with data from the citizen science project Radio Galaxy Zoo. The trained result performed comparably to an algorithm trained on expert cross-identifications, demonstrating the benefit of non-expert labelling in radio astronomy. By examining the theoretical maximum accuracy of this algorithm we showed that existing pilot studies for future surveys were not sufficiently large enough to train machine learning methods. We showed the utility of our cross-identification algorithm by applying it instead to a large survey, Faint Images of the Radio Sky at Twenty Centimeters (FIRST), producing the largest catalogue of cross-identified extended sources available at the time of writing. From this catalogue, we calculated a mid-infrared-divided fractional radio luminosity function as well as an estimate of energy injected into the intergalactic medium by active galactic nuclei jets---one of the first applications of machine learning to radio astronomy to obtain a physics result. A key result from this work was that the limitation in our sample size was not due to the number of radio objects cross-identified but rather by the number of available redshift measurements. Finally, we developed interpretable features for spectropolarimetric measurements of radio sources and used these features to design a machine learning algorithm that can identify Faraday complexity, while the features themselves may be used for other tasks. The methods in this thesis will be applicable to future radio surveys such as the Evolutionary Map of the Universe (EMU) continuum survey and the Polarised Sky Survey of the Universe's Magnetism (POSSUM), as well as surveys produced with the SKA, allowing the development of higher resolution radio luminosity functions, better estimates of the impact of radio galaxies on their environments, faster analysis of polarised surveys, and better quality rotation measure grids