activity
20192026
most citedQSO photometric redshifts using machine learning and neural networks

21 citations · 33 across the 4 of their papers we have counts for

collaborators

5 papers

astro-ph.GA2026

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

C. L. Hale, J. Hamlett, I. H. Whittam +34

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 d…

astro-ph.GA2025

Photometric Redshift Predictions with a Neural Network for DESI Quasars

Jeremy P. Moss, Stephen J. Curran, Yvette C. Perrott

Accurate redshift measurements are essential for studying the evolution of quasi-stellar objects (QSOs) and their role in cosmic structure formation. While spectroscopic redshifts…

astro-ph.CO2022★ 4 cited

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

S. J. Curran, J. P. Moss, Y. C. Perrott

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 Q…

astro-ph.CO2021★ 21 cited

QSO photometric redshifts using machine learning and neural networks

S. J. Curran, J. P. Moss, Y. C. Perrott

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 estim…

astro-ph.GA2019★ 8 cited

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

S. J. Curran, J. P. Moss

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 ex…