Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userClassification and Characterization of Objects from GALEX and SDSS surveys
103
0
0.0
(
0
)
Added by
Luciana Bianchi Dr.
Publication date
2004
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We use the GALEX (Galaxy Evolution Explorer) Medium Imaging Survey (MIS) and All-Sky Imaging Survey (AIS) data available in the first internal release, matched to the SDSS catalogs in the overlapping regions, to classify objects by comparing the multi-band photometry to model colors. We show an example of the advantage of such broad wavelength coverage (GALEX far-UV and near-UV, SDSS ugriz) in classifying objects and augmenting the existing samples and catalogs. From the MIS [AIS] sample over an area of 75 [92] square degrees, we select a total of 1736 [222] QSO candidates at redshift less than 2, significantly extending the number of fainter candidates, and moderately increasing the number of bright objects in the SDSS list of spectroscopically confirmed QSO. Numerous hot stellar objects are also revealed by the UV colors, as expected.
rate research
Read More
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 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.
At present, the precision of deep ultraviolet photometry is somewhat limited by the dearth of faint ultraviolet standard stars. In an effort to improve this situation, we present a uniform catalog of eleven new faint (u sim17) ultraviolet standard stars. High-precision photometry of these stars has been taken from the Sloan Digital Sky Survey and Galaxy Evolution Explorer and combined with new data from the Swift Ultraviolet Optical Telescope to provide precise photometric measures extending from the Near Infrared to the Far Ultraviolet. These stars were chosen because they are known to be hot (20,000 < T_eff < 50,000 K) DA white dwarfs with published Sloan spectra that should be photometrically stable. This careful selection allows us to compare the combined photometry and Sloan spectroscopy to models of pure hydrogen atmospheres to both constrain the underlying properties of the white dwarfs and test the ability of white dwarf models to predict the photometric measures. We find that the photometry provides good constraint on white dwarf temperatures, which demonstrates the ability of Swift/UVOT to investigate the properties of hot luminous stars. We further find that the models reproduce the photometric measures in all eleven passbands to within their systematic uncertainties. Within the limits of our photometry, we find the standard stars to be photometrically stable. This success indicates that the models can be used to calibrate additional filters to our standard system, permitting easier comparison of photometry from heterogeneous sources. The largest source of uncertainty in the model fitting is the uncertainty in the foreground reddening curve, a problem that is especially acute in the UV.
Digital co-addition of astronomical images is a common technique for increasing signal-to-noise and image depth. A modification of this simple technique has been applied to the detection of minor bodies in the Solar System: first stationary objects are removed through the subtraction of a high-SN template image, then the sky motion of the Solar System bodies of interest is predicted and compensated for by shifting pixels in software prior to the co-addition step. This shift-and-stack approach has been applied with great success in directed surveys for minor Solar System bodies. In these surveys, the shifts have been parameterized in a variety of ways. However, these parameterizations have not been optimized and in most cases cannot be effectively applied to data sets with long observation arcs due to objects real trajectories diverging from linear tracks on the sky. This paper presents two novel probabilistic approaches for determining a near-optimum set of shift-vectors to apply to any image set given a desired region of orbital space to search. The first method is designed for short observational arcs, and the second for observational arcs long enough to require non-linear shift-vectors. Using these techniques and other optimizations, we derive optimized grids for previous surveys that have used shift-and-stack approaches to illustrate the improvements that can be made with our method, and at the same time derive new limits on the range of orbital parameters these surveys searched. We conclude with a simulation of a future applications for this approach with LSST, and show that combining multiple nights of data from such next-generation facilities is within the realm of computational feasibility.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
