Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGALEX-SDSS Catalogs for Statistical Studies
111
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
We use the Galaxy Evolution Explorer (GALEX) Medium and All-Sky-Imaging Survey (MIS & AIS) data from the first public data release (GR1), matched to the Sloan Digital Sky Survey (SDSS) DR3 catalog, to perform source classification. The GALEX surveys provide photometry in far- and near-UV bands and the SDSS in five optical bands (u,g,r,i,z). The GR1/DR3 overlapping areas are 363[83]deg^2 for the GALEX AIS[MIS], for sources within the 0.5deg central area of the GALEX fields. Our sample covers mostly |b|>30deg galactic latitudes. We present statistical properties of the GALEX/SDSS matched sources catalog, containing >2x10^6 objects detected in at least one UV band. We classify the matched sources by comparing the seven-band photometry to model colors constructed for different classes of astrophysical objects. For sources with photometric errors <0.3 mag, the corresponding typical AB-magnitude limits are m_FUV~21.5, m_NUV~22.5 for AIS, and m_FUV~24, m_NUV~24.5 for MIS. At AIS depth, the number of Galactic and extragalactic objects are comparable, but the latter predominate in the MIS. Based on our stellar models, we estimate the GALEX surveys detect hot White Dwarfs throughout the Milky Way halo (down to a radius of 0.04 R_sun at MIS depth), providing an unprecedented improvement in the Galactic WD census. Their observed surface density is consistent with Milky Way model predictions. We also select low-redshift QSO candidates, extending the known QSO samples to lower magnitudes, and providing candidates for detailed z~1 follow-up investigations. SDSS optical spectra available for a large subsample confirm the classification for the photometrically selected candidates with 97% purity for single hot stars, ~45%(AIS)/31%(MIS) for binaries containing a hot star and a cooler companion, and about 85% for QSOs.
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.
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 present validation tests of emulator-based halo model method for cosmological parameter inference, assuming hypothetical measurements of the projected correlation function of galaxies, $w_{rm p}(R)$, and the galaxy-galaxy weak lensing, $Delta!Sigma(R)$, from the spectroscopic SDSS galaxies and the Hyper Suprime-Cam Year1 (HSC-Y1) galaxies. To do this, we use textsc{Dark Emulator} developed in Nishimichi et al. based on an ensemble of $N$-body simulations, which is an emulation package enabling a fast, accurate computation of halo clustering quantities for flat-geometry $w$CDM cosmologies. Adopting the halo occupation distribution, the emulator allows us to obtain model predictions of $Delta!Sigma$ and $w_{rm p}$ for the SDSS-like galaxies at a few CPU seconds for an input set of parameters. We present performance and validation of the method by carrying out Markov Chain Monte Carlo analyses of the mock signals measured from a variety of mock catalogs that mimic the SDSS and HSC-Y1 galaxies. We show that the halo model method can recover the underlying true cosmological parameters to within the 68% credible interval, except for the mocks including the assembly bias effect (although we consider the unrealistically-large amplitude of assembly bias effect). Even for the assembly bias mock, we demonstrate that the cosmological parameters can be recovered {it if} the analysis is restricted to scales $Rgtrsim 10~h^{-1}{rm Mpc}$. We also show that, by using a single population of source galaxies to infer the relative strengths of $Delta!Sigma$ for multiple lens samples at different redshifts, the joint probes method allows for self-calibration of photometric redshift errors and multiplicative shear bias. Thus we conclude that the emulator-based halo model method can be safely applied to the HSC-Y1 dataset, achieving a precision of $sigma(S_8)simeq 0.04$.
The Spitzer-SDSS-GALEX Spectroscopic Survey (SSGSS) provides a new sample of 101 star-forming galaxies at z < 0.2 with unprecedented multi-wavelength coverage. New mid- to far-infrared spectroscopy from the Spitzer Space Telescope is added to a rich suite of previous imaging and spectroscopy, including ROSAT, Galaxy Evolution Explorer, Sloan Digital Sky Survey, Two Micron All Sky Survey, and Spitzer/SWIRE. Sample selection ensures an even coverage of the full range of normal galaxy properties, spanning two orders of magnitude in stellar mass, color, and dust attenuation. In this paper we present the SSGSS data set, describe the science drivers, and detail the sample selection, observations, data reduction, and quality assessment. Also in this paper, we compare the shape of the thermal continuum and the degree of silicate absorption of these typical, star-forming galaxies to those of starburst galaxies. We investigate the link between star formation rate, infrared luminosity, and total polycyclic aromatic hydrocarbon luminosity, with a view to calibrating the latter for spectral energy distribution models in photometric samples and at high redshift. Last, we take advantage of the 5-40 micron spectroscopic and far-infrared photometric coverage of this sample to perform detailed fitting of the Draine et al. dust models, and investigate the link between dust mass and star formation history and active galactic nucleus properties.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
