MeerKAT uncovers the physics of an odd radio circle

Odd radio circles (ORCs) are recently-discovered faint diffuse circles of radio emission, of unknown cause, surrounding galaxies at moderate redshift (z ∼0.2-0.6). Here, we present detailed new MeerKAT radio images at 1284 MHz of the first ORC, originally discovered with the Australian Square Kilometre Array Pathfinder, with higher resolution (6 arcsec) and sensitivity (∼2.4 μJy/beam). In addition to the new images, which reveal a complex internal structure consisting of multiple arcs, we also present polarization and spectral index maps. Based on these new data, we consider potential mechanisms that may generate the ORCs

Preliminary results of using k-nearest-neighbor regression to estimate the redshift of radio-selected data sets

In the near future, all-sky radio surveys are set to produce catalogues of tens of millions of sources with limited multiwavelength photometry. Spectroscopic redshifts will only be possible for a small fraction of these new-found sources. In this paper, we provide the first in-depth investigation into the use of k-nearest-neighbor (kNN) regression for the estimation of redshift of these sources. We use Australia Telescope Large Area Survey (ATLAS) radio data, combined with Spitzer Wide-Area Infrared Extragalactic Survey infrared, Dark Energy Survey optical, and Australian Dark Energy Survey spectroscopic survey data. We then reduce the depth of photometry to match what is expected from the upcoming Evolutionary Map of the Universe survey, testing against both data sets. To examine the generalization of our methods, we test one of the subfields of ATLAS against the other. We achieve an outlier rate of ∼10% across all tests, showing that the kNN regression algorithm is an acceptable method of estimating redshift, and would perform better given a sample training set with uniform redshift coverage

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈76% of the highest redshift sources—sources at redshift z > 2.51—as being in either the highest bin (z > 2.51) or second highest (z = 2.25)

Selection of powerful radio galaxies with machine learning

Context. The study of active galactic nuclei (AGNs) is fundamental to discern the formation and growth of supermassive black holes (SMBHs) and their connection with star formation and galaxy evolution. Due to the significant kinetic and radiative energy emitted by powerful AGNs, they are prime candidates to observe the interplay between SMBH and stellar growth in galaxies. Aims. We aim to develop a method to predict the AGN nature of a source, its radio detectability, and redshift purely based on photometry. The use of such a method will increase the number of radio AGNs, allowing us to improve our knowledge of accretion power into an SMBH, the origin and triggers of radio emission, and its impact on galaxy evolution. Methods. We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field. Results. In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with σNMAD=0.07 for HETDEX (σNMAD=0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature. Conclusions. Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate)

Radio observations of supernova remnant G1.9+0.3

We present 1–10 GHz radio continuum flux density, spectral index, polarization, and rotation measure (RM) images of the youngest known Galactic supernova remnant (SNR) G1.9+0.3, using observations from the Australia Telescope Compact Array. We have conducted an expansion study spanning eight epochs between 1984 and 2017, yielding results consistent with previous expansion studies of G1.9+0.3. We find a mean radio continuum expansion rate of (0.78 ± 0.09) per cent yr−1 (or ∼8900 km s−1 at an assumed distance of 8.5 kpc), although the expansion rate varies across the SNR perimetre. In the case of the most recent epoch between 2016 and 2017, we observe faster-than-expected expansion of the northern region. We find a global spectral index for G1.9+0.3 of −0.81 ± 0.02 (76 MHz–10 GHz). Towards the northern region, however, the radio spectrum is observed to steepen significantly (∼−1). Towards the two so-called (east and west) ‘ears’ of G1.9+0.3, we find very different RM values of 400–600 and 100–200 rad m2, respectively. The fractional polarization of the radio continuum emission reaches (19 ± 2) per cent, consistent with other, slightly older, SNRs such as Cas A

Unexpected circular radio objects at high Galactic latitude

We have found a class of circular radio objects in the Evolutionary Map of the Universe Pilot Survey, using the Australian Square Kilometre Array Pathfinder telescope. The objects appear in radio images as circular edge-brightened discs, about one arcmin diameter, that are unlike other objects previously reported in the literature. We explore several possible mechanisms that might cause these objects, but none seems to be a compelling explanation