arXiv subjects

J. P. Moss

Publications and source records attributed to J. P. Moss.

5 recordsLinked to original sources

arXiv2026

MIGHTEE: The Host-Galaxy Associated Catalogue of the Radio Sources in MIGHTEE Continuum Data Release 1

Radio continuum surveys provide samples of active galactic nuclei (AGN) and star forming galaxies (SFGs) to high redshifts, free of biases due to dust obscuration. However, radio detected sources require multi-wavelength counterparts to understand their intrinsic properties (e.g. redshift, stellar mass) and to study the evolution of star formation and AGN activity. In this work we present host galaxy counterparts for the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey continuum Data Release 1 in regions with the best ancillary data (totalling 7.5 sq. deg). We combine statistical cross-matching and visual inspection to identify Ks-band selected host galaxies, and additionally combine multiple radio components into single physical objects, where needed. This results in a combined radio catalogue of ~66 000 sources, with host counterparts and redshifts identified for ~95 per cent of sources in the COSMOS field, ~91 per cent in XMM-LSS and ~90 per cent in CDFS-DEEP. This includes a significant fraction of sources with spectroscopic redshifts within the COSMOS field (~50 per cent), with ~30 and ~20 per cent in the XMM-LSS and CDFS-DEEP fields respectively. Using the cross-matched catalogue, we make an initial identification of radio-excess and star forming galaxies based on comparisons of the radio luminosities to host star formation rates. Using this split as a proxy for radio loud AGN or SFGs, we present expectations for the redshift distributions of these sources, finding broad agreement with those from deep radio luminosity functions and simulated catalogues.

astro-ph.GA↗

arXiv2025

The Evolutionary Map of the Universe: A new radio atlas for the southern hemisphere sky

We present the Evolutionary Map of the Universe (EMU) survey conducted with the Australian Square Kilometre Array Pathfinder (ASKAP). EMU aims to deliver the touchstone radio atlas of the southern hemisphere. We introduce EMU and review its science drivers and key science goals, updated and tailored to the current ASKAP five-year survey plan. The development of the survey strategy and planned sky coverage is presented, along with the operational aspects of the survey and associated data analysis, together with a selection of diagnostics demonstrating the imaging quality and data characteristics. We give a general description of the value-added data pipeline and data products before concluding with a discussion of links to other surveys and projects and an outline of EMU's legacy value.

astro-ph.GA↗

arXiv2022

Redshifts of radio sources in the Million Quasars Catalogue from machine learning

With the aim of using machine learning techniques to obtain photometric redshifts based upon a source's radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44,119 have a spectroscopic redshift, required for model validation, and for which photometry could be obtained. Using the radio spectral properties as features, we fail to find a model which can reliably predict the redshifts, although there is the suggestion that the models improve with the size of the training sample. Using the near-infrared--optical--ultraviolet bands magnitudes, we obtain reliable predictions based on the 12,503 radio sources which have all of the required photometry. From the 80:20 training--validation split, this gives only 2501 validation sources, although training the sample upon our previous SDSS model gives comparable results for all 12,503 sources. This makes us confident that SkyMapper, which will survey southern sky in the u, v, g, r, i, z bands, can be used to predict the redshifts of radio sources detected with the Square Kilometre Array. By using machine learning to impute the magnitudes missing from much of the sample, we can predict the redshifts for 32,698 sources, an increase from 28% to 74% of the sample, at the cost of increasing the outlier fraction by a factor of 1.4. While the "optical" band data prove successful, at this stage we cannot rule out the possibility of a radio photometric redshift, given sufficient data which may be necessary to overcome the relatively featureless radio spectra.

astro-ph.CO↗

arXiv2021

QSO photometric redshifts using machine learning and neural networks

The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here we compare the accuracy of the predicted photometric redshifts obtained from Deep Learning(DL) with the k-Nearest Neighbour (kNN) and the Decision Tree Regression (DTR) algorithms. We find using a combination of near-infrared, visible and ultraviolet magnitudes, trained upon a sample of SDSS QSOs, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of σ = 0.24. Testing on various sub-samples, we find that the DL algorithm generally has lower values of σ, in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with no filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of Δz < 0.1$ up to z ~ 2.5. Applying the DL algorithm trained on our 70,000 strong sample to other independent (radio-selected) datasets, we find σ < 0.36 over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array.

astro-ph.CO↗

arXiv2019

QSO redshift estimates from optical, near-infrared and ultraviolet colours

A simple estimate of the photometric redshift would prove invaluable to forthcoming continuum surveys on the next generation of large radio telescopes, as well as mitigating the existing bias towards the most optically bright sources. While there is a well known correlation between the near-infrared K-band magnitude and redshift for galaxies, we find the K-z relation to break down for samples dominated by quasi-stellar objects (QSOs). We hypothesise that this is due to the additional contribution to the near-infrared flux by the active galactic nucleus (AGN), and, as such, the K-band magnitude can only provide a lower limit to the redshift in the case of active galactic nuclei, which will dominate the radio surveys. From a large optical dataset, we find a tight relationship between the rest-frame (U-K)/(W2-FUV) colour ratio and spectroscopic redshift over a sample of 17,000 sources, spanning z ~ 0.1 - 5. Using the observed-frame ratios of (U K)/(W2-FUV) for redshifts of z > 1, (I-W2)/(W3-U) for 1 < z < 3 and (I-W2.5)/(W4-R) for z > 3, where W2.5 is the 8.0 micron magnitude and the appropriate redshift ranges are estimated from the W2 (4.5 micron) magnitude, we find this to be a robust photometric redshift estimator for quasars. We suggest that the rest-frame U-K colour traces the excess flux from the AGN over this wide range of redshifts, although the W2-FUV colour is required to break the degeneracy.

astro-ph.GA↗