L. Rudnick

University of Minnesota

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R. P. Norris

Commonwealth Scientific and Industrial Research Organisation

H. Andernach

Universidad de Guanajuato

Stanislav S. Shabala

University of Tasmania

O. I. Wong

University of Western Australia

Julie Banfield

Australian National University

B. Koribalski

Australia Telescope National Facility

J. D. Collier

University of Cape Town

A. D. Kapińska

National Radio Astronomy Observatory

D. A. Leahy

University of Calgary

T. J. Galvin

Commonwealth Scientific and Industrial Research Organisation

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Commonwealth Scientific and Industrial Research Organisation

Astronomy and Space

Australia Telescope National Facility

Western Sydney University

Max Planck Society

International Centre for Radio Astronomy Research

National Radio Astronomy Observatory

National Institute for Astrophysics

Netherlands Institute for Radio Astronomy

ARC Centre of Excellence for All-sky Astrophysics

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Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images

Astronomy and Computing (2024)

A. N. Vantyghem T. J. Galvin Biny Sebastian C. P. O’Dea Yjan Gordon

Modern wide field radio surveys typically detect millions of objects. Manual determination of the morphologies is impractical for such a large number of radio sources. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm that projects a many-dimensional dataset onto a two- or three-dimensional lattice of neurons. This dimensionality reduction allows the user to visualize common features of the data better and develop algorithms for classifying objects that are not otherwise possible with large datasets. To this aim, we use the PINK implementation of a SOM. PINK incorporates rotation and flipping invariance so that the SOM algorithm may be applied to astronomical images. In this cookbook we provide instructions for working with PINK, including preprocessing the input images, training the model, and offering lessons learned through experimentation. The problem of imbalanced classes can be improved by careful selection of the training sample and increasing the number of neurons in the SOM (chosen by the user). Because PINK is not scale-invariant, structure can be smeared in the neurons. This can also be improved by increasing the number of neurons in the SOM. We also introduce pyink, a Python package used to read and write PINK binary files, assist in common preprocessing operations, perform standard analyses, visualize the SOM and preprocessed images, and create image-based annotations using a graphical interface. A tutorial is also provided to guide the user through the entire process. We present an application of PINK to VLA Sky Survey (VLASS) images. We demonstrate that the PINK is generally able to group VLASS sources with similar morphology together. We use the results of PINK to estimate the probability that a given source in the VLASS QuickLook Catalogue is actually due to sidelobe contamination.

Python

Self-organizing map

10.1016/j.ascom.2024.100824

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Citations (2)

Electron population aging models for wide-angle tails

New Astronomy Reviews (2002)

A. J. Young L. Rudnick Debora M. Katz-Stone Aileen A. O’Donoghue

Population Ageing

10.1016/s1387-6473(01)00164-6

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Citations (2)

Radio Galaxy Zoo: compact and extended radio source classification with deep learning

Monthly Notices of the Royal Astronomical Society (2018)

Vesna Lukic M. Brüggen Julie Banfield O. I. Wong L. Rudnick

Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in classifying objects in image data. The current work aims to establish when multiple components are present, in the astronomical context of synthesis imaging observations of radio sources. To this effect, we design a convolutional neural network to differentiate between different morphology classes using sources from the Radio Galaxy Zoo (RGZ) citizen science project. In this first step, we focus on exploring the factors that affect the performance of such neural networks, such as the amount of training data, number and nature of layers and the hyperparameters. We begin with a simple experiment in which we only differentiate between two extreme morphologies, using compact and multiple component extended sources. We found that a three convolutional layer architecture yielded very good results, achieving a classification accuracy of 97.4% on a test data set. The same architecture was then tested on a four-class problem where we let the network classify sources into compact and three classes of extended sources, achieving a test achieving a test accuracy of 93.5%. The best-performing convolutional neural network setup has been verified against RGZ Data Release 1 where a final test accuracy of 94.8% was obtained, using both original and augmented images. The use of sigma clipping does not offer a significant benefit overall, except in cases with a small number of training images.

Contextual image classification

Data set

10.1093/mnras/sty163

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Citations (78)

Radio continuum sources behind the Large Magellanic Cloud

Monthly Notices of the Royal Astronomical Society (2021)

M. D. Filipović Ivan S. Bojičić K. Grieve R. P. Norris N. F. H. Tothill

We present a comprehensive multi-frequency catalogue of radio sources behind the Large Magellanic Cloud between 0.2 and 20 GHz, gathered from a combination of new and legacy radio continuum surveys. This catalogue covers an area of $\sim$144~deg$^2$ at angular resolutions from 45 arcsec to $\sim$3 arcmin. We find 6434 discrete radio sources in total, of which 3789 are detected at two or more radio frequencies. We estimate the median spectral index ($\alpha$; where $S_{v}\sim\nu^\alpha$) of $\alpha = -0.89 $ and mean of $-0.88 \pm 0.48$ for 3636 sources detected exclusively at two frequencies (0.843 and 1.384 GHz) with similar resolution (FWHM $\sim$40-45 arcsec). The large frequency range of the surveys makes it an effective tool to investigate Gigahertz Peak Spectrum (GPS), Compact Steep Spectrum (CSS) and Infrared Faint Radio sources populations within our sample. We find 10 GPS candidates with peak frequencies near 5 GHz, from which we estimate their linear size. 1866 sources from our catalogue are (CSS) candidates with $\alpha <-0.8$. We found six candidates for High Frequency Peaker (HFP) sources, whose radio fluxes peak above 5 GHz and no sources with unconstrained peaks and $\alpha~>0.5$. We found optical counterparts for 343 of the radio continuum sources, of which 128have a redshift measurement. Finally, we investigate the population of 123 Infrared Faint Radio Sources (IFRSs) found in this study.

Spectral index

Radio spectrum

Large Magellanic Cloud

10.1093/mnras/stab2249

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Citations (6)

Radio galaxy zoo data release 1: 100,185 radio source classifications from the FIRST and ATLAS surveys

Monthly Notices of the Royal Astronomical Society (2024)

O. I. Wong Avery Garon Matthew J. Alger L. Rudnick Stanislav S. Shabala

Abstract Radio galaxies can extend far beyond the stellar component of their originating host galaxies, and their radio emission can consist of multiple discrete components. Furthermore, the apparent source structure will depend on survey sensitivity, resolution and the observing frequency. Associated discrete radio components and their originating host galaxy are typically identified through a visual comparison of radio and mid-infrared survey images. We present the first data release of Radio Galaxy Zoo, an online citizen science project that enlists the help of citizen scientists to cross-match extended radio sources from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) and the Australia Telescope Large Area Survey (ATLAS) surveys, often with complex structure, to host galaxies in 3.6 μm infrared images from the Wide-field Infrared Survey Explorer (WISE) and the Spitzer Space Telescope. This first data release consists of 100,185 classifications for 99,146 radio sources from the FIRST survey and 583 radio sources from the ATLAS survey. We include two tables for each of the FIRST and ATLAS surveys: 1) the identification of all components making up each radio source; and 2) the cross-matched host galaxies. These classifications have an average reliability of 0.83 based on the weighted consensus levels of our citizen scientists. The reliability of the DR1 catalogue has been further demonstrated through several parallel studies which used the pre-release versions of this catalogue to train and prototype machine learning-based classifiers. We also include a brief description of the radio source populations catalogued by RGZ DR1.

X-shaped radio galaxy

10.1093/mnras/stae2790

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Radio Galaxy Zoo: machine learning for radio source host galaxy cross-identification

Monthly Notices of the Royal Astronomical Society (2018)

Matthew J. Alger Julie Banfield Cheng Soon Ong L. Rudnick O. I. Wong

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe. Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GHz Australian Telescope Large Area Survey (ATLAS) observations of the Chandra Deep Field South (CDFS) and the ESO Large Area ISO Survey South 1 fields by cross-identifying them with the Spitzer Wide-area Infrared Extragalactic survey. We train our method with two sets of data: expert cross-identifications of CDFS from the initial ATLAS data release and crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo. We found that a simple strategy of cross-identifying a radio component with the nearest galaxy performs comparably to our more complex methods, though our estimated best-case performance is near 100 per cent. ATLAS contains 87 complex radio sources that have been cross-identified by experts, so there are not enough complex examples to learn how to cross-identify them accurately. Much larger data sets are therefore required for training methods like ours. We also show that training our method on Radio Galaxy Zoo cross-identifications gives comparable results to training on expert cross-identifications, demonstrating the value of crowdsourced training data.

Identification

10.1093/mnras/sty1308

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Citations (46)

PKS 0812+020: A Quasar Interacting with its Environment

Springer eBooks (1986)

F. D. Ghigo L. Rudnick P. A. Wehinger S. Wyckoff Hyron Spinrad

10.1007/978-94-009-4716-0_46

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Citations (0)

The problems of ageing

New Astronomy Reviews (2002)

L. Rudnick

Confusion

Spectral Properties

10.1016/s1387-6473(01)00162-2

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Citations (9)

Radio Galaxy Zoo: cosmological alignment of radio sources

Monthly Notices of the Royal Astronomical Society (2017)

Omar Contigiani F. de Gasperin G. K. Miley L. Rudnick H. Andernach

We study the mutual alignment of radio sources within two surveys, FIRST and TGSS. This is done by producing two position angle catalogues containing the preferential directions of respectively $30\,059$ and $11\,674$ extended sources distributed over more than $7\,000$ and $17\,000$ square degrees. The identification of the sources in the FIRST sample was performed in advance by volunteers of the Radio Galaxy Zoo project, while for the TGSS sample it is the result of an automated process presented here. After taking into account systematic effects, marginal evidence of a local alignment on scales smaller than $2.5\deg$ is found in the FIRST sample. The probability of this happening by chance is found to be less than $2$ per cent. Further study suggests that on scales up to $1.5\deg$ the alignment is maximal. For one third of the sources, the Radio Galaxy Zoo volunteers identified an optical counterpart. Assuming a flat $\Lambda$CDM cosmology with $\Omega_m = 0.31, \Omega_\Lambda = 0.69$, we convert the maximum angular scale on which alignment is seen into a physical scale in the range $[19, 38]$ Mpc $h_{70}^{-1}$. This result supports recent evidence reported by Taylor and Jagannathan of radio jet alignment in the $1.4$ deg$^2$ ELAIS N1 field observed with the Giant Metrewave Radio Telescope. The TGSS sample is found to be too sparsely populated to manifest a similar signal.

Position angle

10.1093/mnras/stx1977

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Citations (23)

Classifying Complex Faraday Spectra with Convolutional Neural Networks

Monthly Notices of the Royal Astronomical Society (2018)

Shea Brown Brandon M. Bergerud Allison H. Costa B. M. Gaensler J. W. Isbell

Advances in radio spectro-polarimetry offer the possibility to disentangle complex regions where relativistic and thermal plasmas mix in the interstellar and intergalactic media. Recent work has shown that apparently simple Faraday Rotation Measure (RM) spectra can be generated by complex sources. This is true even when the distribution of RMs in the complex source greatly exceeds the errors associated with a single component fit to the peak of the Faraday spectrum. We present a convolutional neural network (CNN) that can differentiate between simple Faraday thin spectra and those that contain multiple or Faraday thick sources. We demonstrate that this CNN, trained for the upcoming Polarisation Sky Survey of the Universe's Magnetism (POSSUM) early science observations, can identify two component sources 99% of the time, provided that the sources are separated in Faraday depth by $>$10% of the FWHM of the Faraday Point Spread Function, the polarized flux ratio of the sources is $>$0.1, and that the Signal-to-Noise radio (S/N) of the primary component is $>$5. With this S/N cut-off, the false positive rate (simple sources mis-classified as complex) is $<$0.3%. Work is ongoing to include Faraday thick sources in the training and testing of the CNN.

Faraday cage

10.1093/mnras/sty2908

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Citations (14)