Prof. Matt Jarvis

Abstract:

The next generation of cosmology experiments will be required to use photometric redshifts rather than spectroscopic redshifts. Obtaining accurate and well-characterized photometric redshift distributions is therefore critical for Euclid, the Large Synoptic Survey Telescope and the Square Kilometre Array. However, determining accurate variance predictions alongside single point estimates is crucial, as they can be used to optimize the sample of galaxies for the specific experiment (e.g. weak lensing, baryon acoustic oscillations, supernovae), trading off between completeness and reliability in the galaxy sample. The various sources of uncertainty in measurements of the photometry and redshifts put a lower bound on the accuracy that any model can hope to achieve. The intrinsic uncertainty associated with estimates is often non-uniform and input-dependent, commonly known in statistics as heteroscedastic noise. However, existing approaches are susceptible to outliers and do not take into account variance induced by non-uniform data density and in most cases require manual tuning of many parameters. In this paper, we present a Bayesian machine learning approach that jointly optimizes the model with respect to both the predictive mean and variance we refer to as Gaussian processes for photometric redshifts (GPz). The predictive variance of the model takes into account both the variance due to data density and photometric noise. Using the SDSS DR12 data, we show that our approach substantially outperforms other machine learning methods for photo-z estimation and their associated variance, such as tpz and annz2. We provide a matlab and python implementations that are available to download at https://github.com/OxfordML/GPz.

Abstract:

We predict the evolution of the radio continuum sky at 1.4 GHz from the Horizon-AGN Adaptive Mesh Refinement (AMR) cosmological hydrodynamical simulation of a cubic volume of the Universe 100h−1 Mpc on a side. With empirically motivated models for the radio continuum emission due to both star formation and Active Galactic Nuclei (AGN), we estimate the contribution of each of these processes to the local radio continuum luminosity function (LF) and describe its evolution up to redshift 4. Despite the simplicity of these models, we find that our predictions for the local luminosity function are fairly consistent with Mauch & Sadler (2007) observations, with the faint end of the luminosity function dominated by star forming galaxies and the bright end by radio loud AGNs. At redshift one, a decent match to Smolcic et al. (2009) VLA data in the COSMOS field can only be achieved when we account for radio continuum emission from AGNs. We predict that the strongest evolution across the peak epoch of cosmic activity happens for low luminosity star forming galaxies L1.4GHz < 1022 W Hz−1 , whose contribution rises until z ∼ 2 and declines at higher redshifts. The contribution of low luminosity AGNs L1.4GHz < 1022 W Hz−1 steadily declines from z = 0 throughout the redshift range, whilst that of radio loud objects with luminosities in the range 1022 W Hz−1 < L1.4GHz < 1024 W Hz−1 rises dramatically until z = 4. Finally, high-luminosity radio loud AGNs, with L1.4GHz > 1024 W Hz−1 show surprisingly little evolution from z = 0 to z = 4.

Abstract:

We present a series of results from a clustering analysis of the first data release of the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey. VIDEO is the only survey currently capable of probing the bulk of stellar mass in galaxies at redshifts corresponding to the peak of star formation on degree scales. Galaxy clustering is measured with the two-point correlation function, which is calculated using a non-parametric kernel-based density estimator. We use our measurements to investigate the connection between the galaxies and the host dark matter halo using a halo occupation distribution methodology, deriving bias, satellite fractions, and typical host halo masses for stellar masses between 10 9.35 and 10 10.85 M ⊙ , at redshifts 0.5 < z < 1.7. Our results show typical halo mass increasing with stellar mass (with moderate scatter) and bias increasing with stellar mass and redshift consistent with previous studies. We find that the satellite fraction increased towards low redshifts, from ~5 per cent at z ~ 1.5 to ~20 per cent at z ~ 0.6. We combine our results to derive the stellar mass-to-halo mass ratio for both satellites and centrals over a range of halo masses and find the peak corresponding to the halo mass with maximum star formation efficiency to be ~2 × 10 12 M ⊙ , finding no evidence for evolution.

Abstract:

We present first results from the KMOS Redshift One Spectroscopic Survey (KROSS), an ongoing large kinematical survey of a thousand, z~1 star forming galaxies, with VLT KMOS. Out of the targeted galaxies (~500 so far), we detect and spatially resolve Ha emission in ~90% and 77% of the sample respectively. Based on the integrated Ha flux measurements and the spatially resolved maps we derive a median star formation rate (SFR) of ~7.0 Msun/yr and a median physical size of = 5.1kpc. We combine the inferred SFRs and effective radii measurements to derive the star formation surface densities ({\Sigma}SFR) and present a "resolved" version of the star formation main sequence (MS) that appears to hold at sub-galactic scales, with similar slope and scatter as the one inferred from galaxy integrated properties. Our data also yield a trend between {\Sigma}SFR and {\Delta}(sSFR) (distance from the MS) suggesting that galaxies with higher sSFR are characterised by denser star formation activity. Similarly, we find evidence for an anti-correlation between the gas phase metallicity (Z) and the {\Delta}(sSFR), suggesting a 0.2dex variation in the metal content of galaxies within the MS and significantly lower metallicities for galaxies above it. The origin of the observed trends between {\Sigma}SFR - {\Sigma}(sSFR) and Z - {\Delta}(sSFR) could be driven by an interplay between variations of the gas fraction or the star formation efficiency of the galaxies along and off the MS. To address this, follow-up observations of the our sample that will allow gas mass estimates are necessary.