ORCID Profile
0000-0001-9188-9359
Current Organisations
University of Hertfordshire
,
University of Nottingham
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Publisher: EDP Sciences
Date: 11-2019
DOI: 10.1051/0004-6361/201935913
Abstract: 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 luminosity, L 150 , as a star-formation rate (SFR) tracer, which is unaffected by dust extinction. Methods. We cross-match the 60 μ m selected Revised IRAS Faint Source Survey Redshift (RIFSCz) catalogue and the 150 MHz selected LOFAR value-added source catalogue in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring Field. We estimate L 150 for the cross-matched sources and compare it with the total infrared (IR) luminosity, L IR , and various SFR tracers. Results. We find a tight linear correlation between log L 150 and log L IR for star-forming galaxies, with a slope of 1.37. The median q IR value (defined as the logarithm of the L IR to L 150 ratio) and its rms scatter of our main s le are 2.14 and 0.34, respectively. We also find that log L 150 correlates tightly with the logarithm of SFR derived from three different tracers, i.e., SFR Hα based on the H α line luminosity, SFR 60 based on the rest-frame 60 μ m luminosity and SFR IR based on L IR , with a scatter of 0.3 dex. Our best-fit relations between L 150 and these SFR tracers are, log L 150 ( L ⊙ ) = 1.35(±0.06) × log SFR H α ( M ⊙ yr −1 ) + 3.20(±0.06), log L 150 ( L ⊙ ) = 1.31(±0.05) × log SFR 60 ( M ⊙ yr −1 ) + 3.14(±0.06), and log L 150 ( L ⊙ ) = 1.37 (±0.05) × log SFR IR ( M ⊙ yr −1 ) + 3.09(±0.05), which show excellent agreement with each other.
Publisher: Oxford University Press (OUP)
Date: 26-10-2019
Abstract: Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z & 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.
Publisher: Oxford University Press (OUP)
Date: 20-07-2016
Publisher: Oxford University Press (OUP)
Date: 14-02-2020
Abstract: 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 active galactic nuclei is important for constraining the binary supermassive black hole population, which is expected as a result of hierarchical galaxy evolution. The age, morphology, and orientation along the line of sight of a given source often result in uncertainties regarding the jet path. This paper presents a new approach for efficient determination of precession parameters using a two-dimensional Markov chain Monte Carlo curve-fitting algorithm that provides us a full posterior probability distribution on the fitted parameters. Applying the method to Cygnus A, we find evidence for previous suggestions that the source is precessing. Interpreting in the context of binary black holes leads to a constraint of parsec scale and likely sub-parsec orbital separation for the putative supermassive binary.
Publisher: Oxford University Press (OUP)
Date: 20-12-2019
Abstract: Accurate methods for reverberation mapping using photometry are highly sought after since they are inherently less resource intensive than spectroscopic techniques. However, the effectiveness of photometric reverberation mapping for estimating black hole masses is sparsely investigated at redshifts higher than z ≈ 0.04. Furthermore, photometric methods frequently assume a d ed random walk (DRW) model, which may not be universally applicable. We perform photometric reverberation mapping using the javelin photometric DRW model for the QSO SDSS-J144645.44+625304.0 at z = 0.351 and estimate the Hβ lag of $65^{+6}_{-1}$ d and black hole mass of $10^{8.22^{+0.13}_{-0.15}}\\, \\mathrm{M_{\\odot }}$. An analysis of the reliability of photometric reverberation mapping, conducted using many thousands of simulated CARMA process light curves, shows that we can recover the input lag to within 6 per cent on average given our target’s observed signal-to-noise of & and average cadence of 14 d (even when DRW is not applicable). Furthermore, we use our suite of simulated light curves to deconvolve aliases and artefacts from our QSO’s posterior probability distribution, increasing the signal-to-noise on the lag by a factor of ∼2.2. We exceed the signal-to-noise of the Sloan Digital Sky Survey Reverberation Mapping Project (SDSS-RM) c aign with a quarter of the observing time per object, resulting in a ∼200 per cent increase in signal-to-noise efficiency over SDSS-RM.
Publisher: Oxford University Press (OUP)
Date: 13-08-2018
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Shaun Read.