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Cross-matching catalogues from radio surveys to catalogues of sources at other wavelengths is extremely hard, because radio sources are often extended, often consist of several spatially separated components, and often no radio component is coincident with the optical/infrared host galaxy. Traditionally, the cross-matching is done by eye, but this does not scale to the millions of radio sources expected from the next generation of radio surveys. We present an innovative automated procedure, using Bayesian hypothesis testing, that models trial radio-source morphologies with putative positions of the host galaxy. This new algorithm differs from an earlier version by allowing more complex radio-source morphologies, and performing a simultaneous fit over a large field. We show that this technique performs well in an unsupervised mode. [ABSTRACT FROM AUTHOR]

The spectroscopic features of white dwarfs are formed in the thin upper layer of their stellar photosphere. These features carry information about the white dwarf's surface temperature, surface gravity, and chemical composition (hereafter 'labels'). Existing methods to determine these labels rely on complex ab-initio theoretical models, which are not always publicly available. Here, we present two techniques to determine atmospheric labels from white dwarf spectra: a generative fitting pipeline that interpolates theoretical spectra with artificial neural networks and a random forest regression model using parameters derived from absorption line features. We test and compare our methods using a large catalogue of white dwarfs from the Sloan Digital Sky Survey (SDSS), achieving the same accuracy and negligible bias as compared to previous studies. We package our techniques into an open-source python module ' wdtools ' that provides a computationally inexpensive way to determine stellar labels from white dwarf spectra observed from any facility. We will actively develop and update our tool as more theoretical models become publicly available. We discuss applications of our tool in its present form to identify interesting outlier white dwarf systems including those with magnetic fields, helium-rich atmospheres, and double-degenerate binaries. [ABSTRACT FROM AUTHOR]

We present the methodology and data behind the photometric redshift data base of the Sloan Digital Sky Survey (SDSS) Data Release 12. We adopt a hybrid technique, empirically estimating the redshift via local regression on a spectroscopic training set, then fitting a spectrum template to obtain K-corrections and absolute magnitudes. The SDSS spectroscopic catalogue was augmented with data from other, publicly available spectroscopic surveys to mitigate target selection effects. The training set is comprised of 1976 978 galaxies, and extends up to redshift z ≈ 0.8, with a useful coverage of up to z ≈ 0.6. We provide photometric redshifts and realistic error estimates for the 208 474 076 galaxies of the SDSS primary photometric catalogue. We achieve an average bias of Δznorm = 5.84 × 10-5, a standard deviation of σ(Δznorm) = 0.0205, and a 3σ outlier rate of Po = 4.11 per cent when cross-validating on our training set. The published redshift error estimates and photometric error classes enable the selection of galaxies with high-quality photometric redshifts. We also provide a supplementary error map that allows additional, sophisticated filtering of the data. [ABSTRACT FROM AUTHOR]

We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the sources' spectral energy distributions (SEDs). We identify and locate star-forming quiescent galaxies, and active galactic nuclei, as well as colour changes due to spectral features, such as the 4000Å break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, such as the need for a lower fraction of M-type stars in certain templates. We compare our clustering-redshift estimates to photometric redshifts and find these two independent estimators to be in good agreement at each limiting magnitude considered. Finally, we present the global clustering-redshift distribution of all Sloan extended sources, showing objects up to z ∼ 0.8. While the overall shape agrees with that inferred from photometric redshifts, the clustering-redshift technique results in a smoother distribution, with no indication of structure in redshift space suggested by the photometricredshift estimates (likely artefacts imprinted by their spectroscopic training set).We also infer a higher fraction of high-redshift objects. The mapping between the four observed colours and redshift can be used to estimate the redshift probability distribution function of individual galaxies. Thiswork is an initial step towards producing a general mapping between redshift and all available observables in the photometric space, including brightness, size, concentration, and ellipticity. [ABSTRACT FROM AUTHOR]

ABSTRACT In this work we present an atlas of composite spectra of galaxies based on the data of the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Galaxies are classified by colour, nuclear activity and star formation activity to calculate average spectra of high signal-to-noise ratio (S/N) and resolution ( at Δλ= 1 Å), using an algorithm that is robust against outliers. Besides composite spectra, we also compute the first five principal components of the distributions in each galaxy class to characterize the nature of variations of individual spectra around the averages. The continua of the composite spectra are fitted with BC03 stellar population synthesis models to extend the wavelength coverage beyond the coverage of the SDSS spectrographs. Common derived parameters of the composites are also calculated: integrated colours in the most popular filter systems, line-strength measurements and continuum absorption indices (including Lick indices). These derived parameters are compared with the distributions of parameters of individual galaxies, and it is shown on many examples that the composites of the atlas cover much of the parameter space spanned by SDSS galaxies. By co-adding thousands of spectra, a total integration time of several months can be reached, which results in extremely low noise composites. The variations in redshift not only allow for extending the spectral coverage bluewards to the original wavelength limit of the SDSS spectrographs, but also make higher spectral resolution achievable. The composite spectrum atlas is available online at . [ABSTRACT FROM AUTHOR]

We use measurements of the H i content, stellar mass and star formation rates (SFRs) in ∼190 massive galaxies with M★ > 1010 M⊙, obtained from the GALEX ( Galaxy Evolution Explorer) Arecibo SDSS (Sloan Digital Sky Survey) survey described in Paper I to explore the global scaling relations associated with the bin-averaged ratio of the SFR over the H i mass (i.e. ΣSFR/Σ MHi), which we call the H i-based star formation efficiency (SFE). Unlike the mean specific star formation rate (sSFR), which decreases with stellar mass and stellar mass surface density, the SFE remains relatively constant across the sample with a value close to SFE = 10−9.5 yr−1 (or an equivalent gas consumption time-scale of ∼3 × 109 yr). Specifically, we find little variation in SFE with stellar mass, stellar mass surface density, NUV − r colour and concentration ( R90/ R50). We interpret these results as an indication that external processes or feedback mechanisms that control the gas supply are important for regulating star formation in massive galaxies. An investigation into the detailed distribution of SFEs reveals that approximately 5 per cent of the sample shows high efficiencies with SFE > 10−9 yr−1, and we suggest that this is very likely due to a deficiency of cold gas rather than an excess SFR. Conversely, we also find a similar fraction of galaxies that appear to be gas-rich for their given sSFR, although these galaxies show both a higher than average gas fraction and lower than average sSFR. Both of these populations are plausible candidates for 'transition' galaxies, showing potential for a change (either decrease or increase) in their sSFR in the near future. We also find that 36 ± 5 per cent of the total H i mass density and 47 ± 5 per cent of the total SFR density are found in galaxies with M★ > 1010 M⊙. [ABSTRACT FROM AUTHOR]

We introduce the GALEX Arecibo SDSS Survey (GASS), an on-going large programme that is gathering high quality H i-line spectra using the Arecibo radio telescope for an unbiased sample of ∼1000 galaxies with stellar masses greater than and redshifts , selected from the Sloan Digital Sky Survey (SDSS) spectroscopic and Galaxy Evolution Explorer ( GALEX) imaging surveys. The galaxies are observed until detected or until a low gas mass fraction limit (1.5–5 per cent) is reached. This paper presents the first Data Release, consisting of ∼20 per cent of the final GASS sample. We use this data set to explore the main scaling relations of the H i gas fraction with galaxy structure and colour. A large fraction (∼60 per cent) of the galaxies in our sample are detected in H i. Even at stellar masses above , the detected fraction does not fall below ∼40 per cent. We find that the atomic gas fraction decreases strongly with stellar mass, stellar surface mass density and colour, but is only weakly correlated with the galaxy bulge-to-disc ratio (as measured by the concentration index of the r-band light). We also find that the fraction of galaxies with significant (more than a few per cent) H i decreases sharply above a characteristic stellar surface mass density of . The fraction of gas-rich galaxies decreases much more smoothly with stellar mass. One of the key goals of GASS is to identify and quantify the incidence of galaxies that are transitioning between the blue, star-forming cloud and the red sequence of passively evolving galaxies. Likely transition candidates can be identified as outliers from the mean scaling relations between and other galaxy properties. We have fitted a plane to the two-dimensional relation between the H i mass fraction, stellar surface mass density and colour. Interesting outliers from this plane include gas-rich red sequence galaxies that may be in the process of regrowing their discs, as well as blue, but gas-poor spirals. [ABSTRACT FROM AUTHOR]

We present a novel technique to overcome the limitations of the applicability of principal component analysis to typical real-life data sets, especially astronomical spectra. Our new approach addresses the issues of outliers, missing information, large number of dimensions and the vast amount of data by combining elements of robust statistics and recursive algorithms that provide improved eigensystem estimates step by step. We develop a generic mechanism for deriving reliable eigenspectra without manual data censoring, while utilizing all the information contained in the observations. We demonstrate the power of the methodology on the attractive collection of the Visible Imaging Multi-Object Spectrograph (VIMOS) Very Large Telescope (VLT) Deep Survey spectra that manifest most of the challenges today, and highlight the improvements over previous workarounds, as well as the scalability of our approach to collections with sizes of the Sloan Digital Sky Survey and beyond. [ABSTRACT FROM AUTHOR]

We discuss the construction of a photometric redshift catalogue of luminous red galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS), emphasizing the principal steps necessary for constructing such a catalogue: (i) photometrically selecting the sample, (ii) measuring photometric redshifts and their error distributions, and (iii) estimating the true redshift distribution. We compare two photometric redshift algorithms for these data and find that they give comparable results. Calibrating against the SDSS and SDSS–2dF (Two Degree Field) spectroscopic surveys, we find that the photometric redshift accuracy isfor redshifts less than 0.55 and worsens at higher redshift (∼ 0.06 for). These errors are caused by photometric scatter, as well as systematic errors in the templates, filter curves and photometric zero-points. We also parametrize the photometric redshift error distribution with a sum of Gaussians and use this model to deconvolve the errors from the measured photometric redshift distribution to estimate the true redshift distribution. We pay special attention to the stability of this deconvolution, regularizing the method with a prior on the smoothness of the true redshift distribution. The methods that we develop are applicable to general photometric redshift surveys. [ABSTRACT FROM AUTHOR]