| 1. |
- Campbell, Heather, et al.
(author)
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COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY
- 2013
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In: Astrophysical Journal. - 0004-637X .- 1538-4357. ; 763:2, s. 88-
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Journal article (peer-reviewed)abstract
- We present the cosmological analysis of 752 photometrically classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey. Our photometric-classification method is based on the SN classification technique of Sako et al., aided by host-galaxy redshifts (0.05 < z < 0.55). SuperNova ANAlysis simulations of our methodology estimate that we have an SN Ia classification efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e. g., Malmquist bias) using simulations. When fitting to a flat.CDM cosmological model, we find that our photometric sample alone gives Omega(m) = 0.24(-0.05)(+0.07) (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on Omega(m) and Omega(Lambda), comparable to those derived from the spectroscopically confirmed Three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics-only result favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological model, and combining with H-0, cosmic microwave background, and luminous red galaxy data, we obtain w = -0.96(-0.10)(+0.10), Omega(m) = 0.29(-0.02)(+0.02), and Omega(k) = 0.00(-0.02)(+0.03)(statistical errors only), which is competitive with similar spectroscopically confirmed SNe Ia analyses. Overall this comparison is reassuring, considering the lower redshift leverage of the SDSS-II SN sample (z < 0.55) and the lack of spectroscopic confirmation used herein. These results demonstrate the potential of photometrically classified SN Ia samples in improving cosmological constraints.
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| 2. |
- Kessler, Richard, et al.
(author)
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Results from the Supernova Photometric Classification Challenge
- 2010
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In: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 122:898, s. 1415-1431
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Journal article (peer-reviewed)abstract
- We report results from the Supernova Photometric Classification Challenge (SNPhotCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rates. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function, and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia-type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN and nine entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe Ia has an efficiency of 0.96 and an SN Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPhotCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS-II, and provided the answer keys so that developers can evaluate their own analysis.
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