Prof. Matt Jarvis

Abstract:

The Herschel Extragalactic Legacy Project (HELP) brings together a vast range of data from many astronomical observatories. Its main focus is on the Herschel data, which maps dust obscured star formation over 1300 deg$^2$. With this unprecedented combination of data sets, it is possible to investigate how the star formation vs stellar mass relation (main-sequence) of star-forming galaxies depends on environment. In this pilot study we explore this question between 0.1 < z < 3.2 using data in the COSMOS field. We estimate the local environment from a smoothed galaxy density field using the full photometric redshift probability distribution. We estimate star formation rates by stacking the SPIRE data from the Herschel Multi-tiered Extragalactic Survey (HerMES). Our analysis rules out the hypothesis that the main-sequence for star-forming systems is independent of environment at 1.5 < z < 2, while a simple model in which the mean specific star formation rate declines with increasing environmental density gives a better description. However, we cannot exclude a simple hypothesis in which the main-sequence for star-forming systems is independent of environment at z < 1.5 and z > 2. We also estimate the evolution of the star formation rate density in the COSMOS field and our results are consistent with previous measurements at z < 1.5 and z > 2 but we find a $1.4^{+0.3}_{-0.2}$ times higher peak value of the star formation rate density at $z \sim 1.9$.

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.