The Square Kilometre Array (SKA)

Abstract:

We present a Bayesian stacking technique to directly measure the H i mass function (HIMF) and its evolution with redshift using galaxies formally below the nominal detection threshold. We generate galaxy samples over several sky areas given an assumed HIMF described by a Schechter function and simulate the H i emission lines with different levels of background noise to test the technique. We use Multinest to constrain the parameters of the HIMF in a broad redshift bin, demonstrating that the HIMF can be accurately reconstructed, using the simulated spectral cube far below the H i mass limit determined by the 5σ flux-density limit, i.e. down to MHI = 107.5 M⊙ over the redshift range 0 < z < 0.55 for this particular simulation, with a noise level similar to that expected for the MIGHTEE survey. We also find that the constraints on the parameters of the Schechter function, φ⋆, M⋆ and α can be reliably fit, becoming tighter as the background noise decreases as expected, although the constraints on the redshift evolution are not significantly affected. All the parameters become better constrained as the survey area increases. In summary, we provide an optimal method for estimating the H i mass at cosmological distances that allows us to constrain the H i mass function below the detection threshold in forthcoming H i surveys. This study is a first step towards the measurement of the HIMF at high (z > 0.1) redshifts.

Abstract:

Hydrodynamical cosmological simulations have recently made great advances in reproducing galaxy mass assembly over cosmic time - as often quantified from the comparison of their predicted stellar mass functions to observed stellar mass functions from data. In this paper we compare the clustering of galaxies from the hydrodynamical cosmological simulated lightcone Horizon-AGN, to clustering measurements from the VIDEO survey observations. Using mocks built from a VIDEO-like photometry, we first explore the bias introduced into clustering measurements by using stellar masses and redshifts derived from SED-fitting, rather than the intrinsic values. The propagation of redshift and mass statistical and systematic uncertainties in the clustering measurements causes us to underestimate the clustering amplitude. We find then that clustering and halo occupation distribution (HOD) modelling results are qualitatively similar in Horizon-AGN and VIDEO. However at low stellar masses Horizon-AGN underestimates the observed clustering by up to a factor of ~3, reflecting the known excess stellar mass to halo mass ratio for Horizon-AGN low mass haloes, already discussed in previous works. This reinforces the need for stronger regulation of star formation in low mass haloes in the simulation. Finally, the comparison of the stellar mass to halo mass ratio in the simulated catalogue, inferred from angular clustering, to that directly measured from the simulation, validates HOD modelling of clustering as a probe of the galaxy-halo connection.

Abstract:

ABSTRACT Time-series analysis allows for the determination of the Power Spectral Density (PSD) and Probability Density Function (PDF) for astrophysical sources. The former of these illustrates the distribution of power at various time-scales, typically taking a power-law form, while the latter characterizes the distribution of the underlying stochastic physical processes, with Gaussian and lognormal functional forms both physically motivated. In this paper, we use artificial time series generated using the prescription of Timmer & Koenig to investigate connections between the PDF and PSD. PDFs calculated for these artificial light curves are less likely to be well described by a Gaussian functional form for steep (Γ⪆1) PSD indices due to weak non-stationarity. Using the Fermi LAT monthly light curve of the blazar PKS2155-304 as an example, we prescribe and calculate a false positive rate that indicates how likely the PDF is to be attributed an incorrect functional form. Here, we generate large numbers of artificial light curves with intrinsically normally distributed PDFs and with statistical properties consistent with observations. These are used to evaluate the probabilities that either Gaussian or lognormal functional forms better describe the PDF. We use this prescription to show that PKS2155-304 requires a high prior probability of having a normally distributed PDF, $P(\rm {G})~$ ≥ 0.82, for the calculated PDF to prefer a Gaussian functional form over a lognormal. We present possible choices of prior and evaluate the probability that PKS2155-304 has a lognormally distributed PDF for each.