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Abstract:

We present radio active galactic nuclei (AGN) luminosity functions over the redshift range 0.005 < z < 0.75. The sample from which the luminosity functions are constructed is an optical spectroscopic survey of radio galaxies, identified from matched Faint Images of the Radio Sky at Twenty-cm survey (FIRST) sources and Sloan Digital Sky Survey images. The radio AGN are separated into low-excitation radio galaxies (LERGs) and high-excitation radio galaxies (HERGs) using the optical spectra. We derive radio luminosity functions for LERGs and HERGs separately in the three redshift bins (0.005 < z < 0.3, 0.3 < z < 0.5 and 0.5 < z < 0.75). The radio luminosity functions can be well described by a double power law. Assuming this double power-law shape the LERG population displays little or no evolution over this redshift range evolving as ∼(1+z)0.06+0.17−0.18 assuming pure density evolution or ∼(1+z)0.46+0.22−0.24 assuming pure luminosity evolution. In contrast, the HERG population evolves more rapidly, best fitted by ∼(1+z)2.93+0.46−0.47 assuming a double power-law shape and pure density evolution. If a pure luminosity model is assumed, the best-fitting HERG evolution is parametrized by ∼(1+z)7.41+0.79−1.33 . The characteristic break in the radio luminosity function occurs at a significantly higher power (≳1 dex) for the HERG population in comparison to the LERGs. This is consistent with the two populations representing fundamentally different accretion modes.

Abstract:

We present Low-Frequency Array (LOFAR) High-Band Array observations of the Herschel-ATLAS North Galactic Pole survey area. The survey we have carried out, consisting of four pointings covering around 142 deg2 of sky in the frequency range 126–173 MHz, does not provide uniform noise coverage but otherwise is representative of the quality of data to be expected in the planned LOFAR wide-area surveys, and has been reduced using recently developed ‘facet calibration’ methods at a resolution approaching the full resolution of the data sets (∼10 × 6 arcsec) and an rms off-source noise that ranges from 100 μJy beam−1 in the centre of the best fields to around 2 mJy beam−1 at the furthest extent of our imaging. We describe the imaging, cataloguing and source identification processes, and present some initial science results based on a 5σ source catalogue. These include (i) an initial look at the radio/far-infrared correlation at 150 MHz, showing that many Herschel sources are not yet detected by LOFAR; (ii) number counts at 150 MHz, including, for the first time, observational constraints on the numbers of star-forming galaxies; (iii) the 150-MHz luminosity functions for active and star-forming galaxies, which agree well with determinations at higher frequencies at low redshift, and show strong redshift evolution of the star-forming population; and (iv) some discussion of the implications of our observations for studies of radio galaxy life cycles.

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.