activity
20182020
most citedGalaxy morphological classification in deep-wide surveys via unsupervised machine learning

84 citations · 98 across the 3 of their papers we have counts for

collaborators

5 papers

astro-ph.GA20209 cited

A Markov Chain Monte Carlo approach for measurement of jet precession in radio-loud active galactic nuclei

Maya A. Horton, Martin J. Hardcastle, Shaun C. Read +1

Jet precession can reveal the presence of binary systems of supermassive black holes. The ability to accurately measure the parameters of jet precession from radio-loud AGN is impo…

astro-ph.GA20195 cited

The Performance of Photometric Reverberation Mapping at High Redshift and the Reliability of Damped Random Walk Models

S. C. Read, D. J. B. Smith, M. J. Jarvis +1

Accurate methods for reverberation mapping using photometry are highly sought after since they are inherently less resource intensive than spectroscopic techniques. However, the ef…

astro-ph.GA201984 cited

Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

Garreth Martin, Sugata Kaviraj, Alex Hocking +2

Galaxy morphology is a fundamental quantity, that is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmo…

astro-ph.GA2019

A LOFAR-IRAS cross-match study: the far-infrared radio correlation and the 150-MHz luminosity as a star-formation rate

L. Wang, F. Gao, K. J. Duncan +15

Aims. We aim to study the far-infrared radio correlation (FIRC) at 150 MHz in the local Universe (at a median redshift z~0:05) and improve the use of the rest-frame 150-MHz luminos…

astro-ph.GA2018

The Far-Infrared Radio Correlation at low radio frequency with LOFAR/H-ATLAS

S. C. Read, D. J. B. Smith, G. Gürkan +14

The radio and far-infrared luminosities of star-forming galaxies are tightly correlated over several orders of magnitude; this is known as the far-infrared radio correlation (FIRC)…