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The Sloan Digital Sky Survey-II Supernova Survey: Technical Summary

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 Added by Masao Sako
 Publication date 2007
  fields Physics
and research's language is English




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The Sloan Digital Sky Survey-II (SDSS-II) has embarked on a multi-year project to identify and measure light curves for intermediate-redshift (0.05 < z < 0.35) Type Ia supernovae (SNe Ia) using repeated five-band (ugriz) imaging over an area of 300 sq. deg. The survey region is a stripe 2.5 degrees wide centered on the celestial equator in the Southern Galactic Cap that has been imaged numerous times in earlier years, enabling construction of a deep reference image for discovery of new objects. Supernova imaging observations are being acquired between 1 September and 30 November of 2005-7. During the first two seasons, each region was imaged on average every five nights. Spectroscopic follow-up observations to determine supernova type and redshift are carried out on a large number of telescopes. In its first two three-month seasons, the survey has discovered and measured light curves for 327 spectroscopically confirmed SNe Ia, 30 probable SNe Ia, 14 confirmed SNe Ib/c, 32 confirmed SNe II, plus a large number of photometrically identified SNe Ia, 94 of which have host-galaxy spectra taken so far. This paper provides an overview of the project and briefly describes the observations completed during the first two seasons of operation.



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This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg2 area along the celestial equator. This data release is comprised of all transient sources brighter than r~22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star-formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1443 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 677 purely-photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat Lambda-CDM cosmology, we determine Omega_M = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigmas.
The Sloan Digital Sky Survey Reverberation Mapping project (SDSS-RM) is a dedicated multi-object RM experiment that has spectroscopically monitored a sample of 849 broad-line quasars in a single 7 deg$^2$ field with the SDSS-III BOSS spectrograph. The RM quasar sample is flux-limited to i_psf=21.7 mag, and covers a redshift range of 0.1<z<4.5. Optical spectroscopy was performed during 2014 Jan-Jul dark/grey time, with an average cadence of ~4 days, totaling more than 30 epochs. Supporting photometric monitoring in the g and i bands was conducted at multiple facilities including the CFHT and the Steward Observatory Bok telescopes in 2014, with a cadence of ~2 days and covering all lunar phases. The RM field (RA, DEC=14:14:49.00, +53:05:00.0) lies within the CFHT-LS W3 field, and coincides with the Pan-STARRS 1 (PS1) Medium Deep Field MD07, with three prior years of multi-band PS1 light curves. The SDSS-RM 6-month baseline program aims to detect time lags between the quasar continuum and broad line region (BLR) variability on timescales of up to several months (in the observed frame) for ~10% of the sample, and to anchor the time baseline for continued monitoring in the future to detect lags on longer timescales and at higher redshift. SDSS-RM is the first major program to systematically explore the potential of RM for broad-line quasars at z>0.3, and will investigate the prospects of RM with all major broad lines covered in optical spectroscopy. SDSS-RM will provide guidance on future multi-object RM campaigns on larger scales, and is aiming to deliver more than tens of BLR lag detections for a homogeneous sample of quasars. We describe the motivation, design and implementation of this program, and outline the science impact expected from the resulting data for RM and general quasar science.
The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 sq. deg. region along the celestial equator during its first two seasons of a three-season campaign. Multi-band (ugriz) light curves were measured for most of the sources, which include solar system objects, Galactic variable stars, active galactic nuclei, supernovae (SNe), and other astronomical transients. The imaging survey is augmented by an extensive spectroscopic follow-up program to identify SNe, measure their redshifts, and study the physical conditions of the explosions and their environment through spectroscopic diagnostics. During the survey, light curves are rapidly evaluated to provide an initial photometric type of the SNe, and a selected sample of sources are targeted for spectroscopic observations. In the first two seasons, 476 sources were selected for spectroscopic observations, of which 403 were identified as SNe. For the Type Ia SNe, the main driver for the Survey, our photometric typing and targeting efficiency is 90%. Only 6% of the photometric SN Ia candidates were spectroscopically classified as non-SN Ia instead, and the remaining 4% resulted in low signal-to-noise, unclassified spectra. This paper describes the search algorithm and the software, and the real-time processing of the SDSS imaging data. We also present the details of the supernova candidate selection procedures and strategies for follow-up spectroscopic and imaging observations of the discovered sources.
We present measurements of the Hubble diagram for 103 Type Ia supernovae (SNe) with redshifts 0.04 < z < 0.42, discovered during the first season (Fall 2005) of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey. These data fill in the redshift desert between low- and high-redshift SN Ia surveys. We combine the SDSS-II measurements with new distance estimates for published SN data from the ESSENCE survey, the Supernova Legacy Survey, the Hubble Space Telescope, and a compilation of nearby SN Ia measurements. Combining the SN Hubble diagram with measurements of Baryon Acoustic Oscillations from the SDSS Luminous Red Galaxy sample and with CMB temperature anisotropy measurements from WMAP, we estimate the cosmological parameters w and Omega_M, assuming a spatially flat cosmological model (FwCDM) with constant dark energy equation of state parameter, w. For the FwCDM model and the combined sample of 288 SNe Ia, we find w = -0.76 +- 0.07(stat) +- 0.11(syst), Omega_M = 0.306 +- 0.019(stat) +- 0.023(syst) using MLCS2k2 and w = -0.96 +- 0.06(stat) +- 0.12(syst), Omega_M = 0.265 +- 0.016(stat) +- 0.025(syst) using the SALT-II fitter. We trace the discrepancy between these results to a difference in the rest-frame UV model combined with a different luminosity correction from color variations; these differences mostly affect the distance estimates for the SNLS and HST supernovae. We present detailed discussions of systematic errors for both light-curve methods and find that they both show data-model discrepancies in rest-frame $U$-band. For the SALT-II approach, we also see strong evidence for redshift-dependence of the color-luminosity parameter (beta). Restricting the analysis to the 136 SNe Ia in the Nearby+SDSS-II samples, we find much better agreement between the two analysis methods but with larger uncertainties.
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