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Register a new userCross-Identification Performance from Simulated Detections: GALEX and SDSS
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We investigate the quality of associations of astronomical sources from multi-wavelength observations using simulated detections that are realistic in terms of their astrometric accuracy, small-scale clustering properties and selection functions. We present a general method to build such mock catalogs for studying associations, and compare the statistics of cross-identifications based on angular separation and Bayesian probability criteria. In particular, we focus on the highly relevant problem of cross-correlating the ultraviolet Galaxy Evolution Explorer (GALEX) and optical Sloan Digital Sky Survey (SDSS) surveys. Using refined simulations of the relevant catalogs, we find that the probability thresholds yield lower contamination of false associations, and are more efficient than angular separation. Our study presents a set of recommended criteria to construct reliable cross-match catalogs between SDSS and GALEX with minimal artifacts.
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We construct a pipeline for simulating weak lensing cosmology surveys with the Square Kilometre Array (SKA), taking as inputs telescope sensitivity curves; correlated source flux, size and redshift distributions; a simple ionospheric model; source redshift and ellipticity measurement errors. We then use this simulation pipeline to optimise a 2-year weak lensing survey performed with the first deployment of the SKA (SKA1). Our assessments are based on the total signal-to-noise of the recovered shear power spectra, a metric that we find to correlate very well with a standard dark energy figure of merit. We first consider the choice of frequency band, trading off increases in number counts at lower frequencies against poorer resolution; our analysis strongly prefers the higher frequency Band 2 (950-1760 MHz) channel of the SKA-MID telescope to the lower frequency Band 1 (350-1050 MHz). Best results would be obtained by allowing the centre of Band 2 to shift towards lower frequency, around 1.1 GHz. We then move on to consider survey size, finding that an area of 5,000 square degrees is optimal for most SKA1 instrumental configurations. Finally, we forecast the performance of a weak lensing survey with the second deployment of the SKA. The increased survey size (3$pi$,steradian) and sensitivity improves both the signal-to-noise and the dark energy metrics by two orders of magnitude.
We present a detailed study of the Galaxy Evolution Explorers photometric catalogs with special focus on the statistical properties of the All-sky and Medium Imaging Surveys. We introduce the concept of primaries to resolve the issue of multiple detections and follow a geometric approach to define clean catalogs with well-understood selection functions. We cross-identify the GALEX sources (GR2+3) with Sloan Digital Sky Survey (DR6) observations, which indirectly provides an invaluable insight about the astrometric model of the UV sources and allows us to revise the band merging strategy. We derive the formal description of the GALEX footprints as well as their intersections with the SDSS coverage along with analytic calculations of their areal coverage. The crossmatch catalogs are made available for the public. We conclude by illustrating the implementation of typical selection criteria in SQL for catalog subsets geared toward statistical analyses, e.g., correlation and luminosity function studies.
We discuss a novel approach to identifying cosmic events in separate and independent observations. In our focus are the true events, such as supernova explosions, that happen once, hence, whose measurements are not repeatable. Their classification and analysis have to make the best use of all the available data. Bayesian hypothesis testing is used to associate streams of events in space and time. Probabilities are assigned to the matches by studying their rates of occurrence. A case study of Type Ia supernovae illustrates how to use lightcurves in the cross-identification process. Constraints from realistic lightcurves happen to be well-approximated by Gaussians in time, which makes the matching process very efficient. Model-dependent associations are computationally more demanding but can further boost our confidence.
Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2%) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.
We measure the projected spatial correlation function w_p(r_p) from a large sample combining GALEX ultraviolet imaging with the SDSS spectroscopic sample. We study the dependence of the clustering strength for samples selected on (NUV - r)_abs color, specific star formation rate (SSFR), and stellar mass. We find that there is a smooth transition in the clustering of galaxies as a function of this color from weak clustering among blue galaxies to stronger clustering for red galaxies. The clustering of galaxies within the green valley has an intermediate strength, and is consistent with that expected from galaxy groups. The results are robust to the correction for dust extinction. The comparison with simple analytical modeling suggests that the halo occupation number increases with older star formation epochs. When splitting according to SSFR, we find that the SSFR is a more sensitive tracer of environment than stellar mass.
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