Measuring the Physical Properties of Distant Galaxies and Black Holes in the Era of Surveys

Abstract

In the era of deep and wide-field surveys (e.g. SDSS, LSST, LOFAR, SKA), we have access to an ever-increasing volume of multi-wavelength data for millions of galaxies both locally and at high redshifts. However, inferring the intrinsic properties of the whole population of galaxies requires robust statistical techniques and an understanding of observational bias.

In this thesis, I present a study of the Far-Infrared Radio Correlation (FIRC) – a relation which is widely used to infer star-formation rates from otherwise featureless radio sources. Using LOFAR 150MHz, FIRST 1:4GHz, and Herschel infrared luminosities derived from the new LOFAR/H-ATLAS catalogue, we investigate possible variation in the monochromatic (250mm) FIRC at low and high radio frequencies. Although the average FIRC at high radio frequency is consistent with expectations based on a standard power-law radio spectrum, the average correlation at 150MHz is not. We see evidence for redshift evolution of the FIRC at 150MHz, and find that the FIRC varies with stellar mass, dust temperature and specific star formation rate, whether the latter is probed using MAGPHYS fitting, or using mid-infrared colour as a proxy. We can explain the variation, to within σ using a Bayesian partial correlation technique. This work was published as Read et al. (2018) in the Monthly Notices of the Royal Astronomical Society.

Identifying an opportunity to increase in the efficiency of black-hole mass estimations, we perform photometric reverberation mapping using the Javelin photometric damped random walk model for the QSO SDSS J144645.44 +625304.0 at ɀ = 0:351 and estimate the Hβ lag of 72 +5-1 days and black hole mass of 108:28 +0.12-0.07 Mʘ. An analysis of the reliability of photometric reverberation mapping conducted using many thousands of simulated light curves shows that we can recover any input lag less than a third of the duration of our observing campaign to within 4per cent on average given our target’s observed signal-to-noise of > 20 and cadence of 14 days. We use our suite of simulated light curves to deconvolve artefacts from the QSO’s posterior lag distribution, increasing the signal-to-noise by a factor of ~3. We exceed the signal-to-noise of the Sloan Digital Sky Survey Reverberation Mapping Project (SDSS-RM) campaign with a quarter of the observing time resulting in a ~310 per cent per cent increase in SNR efficiency over SDSS-RM.

Finally, I present a study of the radio luminosity star-formation rate relation directly with the LOFAR Two Metre Sky Survey (LoTSS) DR1, in an effort to understand the mass dependency of the L150MHz - SFR slope reported by Gürkan et al. (2018). Building on our previous study of the FIRC, we develop a fast, generalised algorithm to recover Complete And Noiseless Distributions from Incomplete Data (CANDID). We find that the mass dependency is real and in agreement with previous estimations in the literature when we include the effects of selection biases present in the LoTSS DR1 sample. We also propose that type-Ia supernovae may contribute to a L150MHz excess and construct a joint distribution of our LoTSS observations and the Horizon AGN simulation to test this.