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Register a new userPhotometric Cross-Calibration of the SDSS Stripe 82 Standard Stars catalogue with Gaia EDR3, and Comparison with Pan-STARRS1, DES, CFIS and GALEX catalogues
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Authors
Karun Thanjavur
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We extend the SDSS Stripe 82 Standard Stars Catalog with post-2007 SDSS imaging data. This improved version lists averaged SDSS ugriz photometry for nearly a million stars brighter than r~22 mag. With 2-3x more measurements per star, random errors are 1.4-1.7x smaller than in the original catalog, and about 3x smaller than for individual SDSS runs. Random errors in the new catalog are ~< 0.01 mag for stars brighter than 20.0, 21.0, 21.0, 20.5, and 19.0 mag in u, g, r, i, and z-bands, respectively. We achieve this error threshold by using the Gaia Early Data Release 3 (EDR3) Gmag photometry to derive gray photometric zeropoint corrections, as functions of R.A. and Declination, for the SDSS catalog, and use the Gaia BP-RP colour to derive corrections in the ugiz bands, relative to the r-band. The quality of the recalibrated photometry, tested against Pan-STARRS1, DES, CFIS and GALEX surveys, indicates spatial variations of photometric zeropoints <=0.01 mag (RMS), with typical values of 3-7 millimag in the R.A., and 1-2 millimag in the Declination directions, except for <~6 millimag scatter in the u-band. We also report a few minor photometric problems with other surveys considered here, including a magnitude-dependent ~0.01 mag bias between 16 < G_Gaia < 20 in the Gaia EDR3. Our new, publicly available catalog offers robust calibration of ugriz photometry below 1% level, and will be helpful during the commissioning of the Vera C. Rubin Observatory Legacy Survey of Space and Time.
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We compare Stetsons photometric standards with measurements listed in a standard star catalog constructed using repeated SDSS imaging observations. The SDSS catalog includes over 700,000 candidate standard stars from the equatorial stripe 82 (|Dec|<1.266 deg) in the RA range 20h 34 to 4h 00, and with the $r$ band magnitudes in the range 14--21. The distributions of measurements for individual sources demonstrate that the SDSS photometric pipeline correctly estimates random photometric errors, which are below 0.01 mag for stars brighter than (19.5, 20.5, 20.5, 20, 18.5) in ugriz, respectively (about twice as good as for individual SDSS runs). We derive mean photometric transformations between the SDSS gri and the BVRI system using 1165 Stetson stars found in the equatorial stripe 82, and then study the spatial variation of the difference in zeropoints between the two catalogs. Using third order polynomials to describe the color terms, we find that photometric measurements for main-sequence stars can be transformed between the two systems with systematic errors smaller than a few millimagnitudes. The spatial variation of photometric zeropoints in the two catalogs typically does not exceed 0.01 magnitude. Consequently, the SDSS Standard Star Catalog for Stripe 82 can be used to calibrate new data in both the SDSS ugriz and the BVRI systems with a similar accuracy.
We measure quasar variability using the Panoramic Survey Telescope and Rapid Response System 1 Survey (Pan-STARRS1 or PS1) and the Sloan Digital Sky Survey (SDSS) and establish a method of selecting quasars via their variability in 10,000 square degree surveys. We use 100,000 spectroscopically confirmed quasars that have been well measured in both PS1 and SDSS and take advantage of the decadal time scales that separate SDSS measurements and PS1 measurements. A power law model fits the data well over the entire time range tested, 0.01 to 10 years. Variability in the current PS1-SDSS dataset can efficiently distinguish between quasars and non-varying objects. It improves the purity of a griz quasar color cut from 4.1% to 48% while maintaining 67% completeness. Variability will be very effective at finding quasars in datasets with no u band and in redshift ranges where exclusively photometric selection is not efficient. We show that quasars rest-frame ensemble variability, measured as a root mean squared in delta magnitudes, is consistent with V(z, L, t) = A0 (1+z)^0.37 (L/L0)^-0.16 (t/1yr)^0.246 , where L0 = 10^46 ergs^-1 and A0 = 0.190, 0.162, 0.147 or 0.141 in the gP1 , rP1 , iP1 or zP1 filter, respectively. We also fit across all four filters and obtain median variability as a function of z, L and lambda as V(z, L, lambda, t) = 0.079(1 + z)^0.15 (L/L0 )^-0.2 (lambda/1000 nm)^-0.44 (t/1yr)^0.246 .
Although a catalogue of synthetic RGB magnitudes, providing photometric data for a sample of 1346 bright stars, has been recently published, its usefulness is still limited due to the small number of reference stars available, considering that they are distributed throughout the whole celestial sphere, and the fact that they are restricted to Johnson V < 6.6 mag. This work presents synthetic RGB magnitudes for ~15 million stars brighter than Gaia G = 18 mag, making use of a calibration between the RGB magnitudes of the reference bright star sample and the corresponding high quality photometric G, G_BP and G_RP magnitudes provided by the Gaia EDR3. The calibration has been restricted to stars exhibiting -0.5 < G_BP - G_RP < 2.0 mag, and aims to predict RGB magnitudes within an error interval of $pm 0.1$ mag. Since the reference bright star sample is dominated by nearby stars with slightly undersolar metallicity, systematic variations in the predictions are expected, as modelled with the help of stellar atmosphere models. These deviations are constrained to the $pm 0.1$ mag interval when applying the calibration only to stars scarcely affected by interstellar extinction and with metallicity compatible with the median value for the bright star sample. The large number of Gaia sources available in each region of the sky should guarantee high-quality RGB photometric calibrations.
Context: Blue horizontal-branch stars are very old objects that can be used as markers in studies of the Galactic structure and formation history. To create a clean sky catalogue of blue horizontal-branch stars, we cross-matched the Gaia data release 2 (DR2) dataset with existing reference catalogues to define selection criteria based on Gaia DR2 parameters. Following the publication of Gaia early data release 3 (EDR3), these methods were verified and subsequently applied to this latest release. Aims: The purpose of this catalogue is to identify a set of blue horizontal-branch star candidates that have been selected using photometric and astrometric observations and exhibits a low contamination rate. Methods: We cross-matched reference blue horizontal-branch datasets with the Gaia DR2 database and defined two sets of selection criteria. Firstly, in Gaia DR2 - colour and absolute G magnitude space, and secondly, in Gaia DR2 - colour and reduced proper motion space. The main-sequence contamination in both subsets of the catalogue was reduced, at the expense of completeness, by concentrating on the Milky Ways Galactic halo, where relatively young main-sequence stars were not expected. Results: We present a catalogue, based on Gaia EDR3, of 57,377 blue horizontal-branch stars. The Gaia EDR3 parallax was used in selecting 16,794 candidates and the proper motions were used to identify a further 40,583 candidates.
The Pan-STARRS1 survey is collecting multi-epoch, multi-color observations of the sky north of declination -30 deg to unprecedented depths. These data are being photometrically and astrometrically calibrated and will serve as a reference for many other purposes. In this paper we present our determination of the Pan-STARRS photometric system: gp1, rp1, ip1, zp1, yp1, and wp1. The Pan-STARRS photometric system is fundamentally based on the HST Calspec spectrophotometric observations, which in turn are fundamentally based on models of white dwarf atmospheres. We define the Pan-STARRS magnitude system, and describe in detail our measurement of the system passbands, including both the instrumental sensitivity and atmospheric transmission functions. Byproducts, including transformations to other photometric systems, galactic extinction, and stellar locus are also provided. We close with a discussion of remaining systematic errors.
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