COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY

Campbell, H.; D’Andrea, C. B.; Nichol, R. C.; Šako, M.; Smith, M.; Lampeitl, H.; Olmstead, Matthew D.; Bassett, Bruce A.; Biswas, Rahul; Brown, P. J.; Cinabro, D.; Dawson, Kyle; Dilday, Benjamin; Foley, R. J.; Frieman, Joshua A.; Garnavich, P.; Hložek, Renée; Jha, Saurabh W.; Kuhlmann, S.; Kunz, M.; Marriner, J.; Miquel, R.; Richmond, M.; Riess, Adam G.; Schneider, Donald P.; Sollerman, J.; Taylor, Maureen; Zhao, Gong-Bo

doi:10.1088/0004-637x/763/2/88

Cited by 112 publications

(139 citation statements)

References 131 publications

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“…A moderate amount of real-time supernova spectroscopy may then suffice to assess efficiency and biases. The recent SDSS-II analysis by Campbell et al (2012) puts this approach into practice, illustrating its promise and its challenges.…”

Section: Figurementioning

confidence: 99%

Observational probes of cosmic acceleration

et al. 2013

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The accelerating expansion of the universe is the most surprising cosmological discovery in many decades, implying that the universe is dominated by some form of "dark energy" with exotic physical properties, or that Einstein's theory of gravity breaks down on cosmological scales. The profound implications of cosmic acceleration have inspired ambitious efforts to understand its origin, with experiments that aim to measure the history of expansion and growth of structure with percent-level precision or higher. We review in detail the four most well established methods for making such measurements: Type Ia supernovae, baryon acoustic oscillations (BAO), weak gravitational lensing, and the abundance of galaxy clusters. We pay particular attention to the systematic uncertainties in these techniques and to strategies for controlling them at the level needed to exploit "Stage IV" dark energy facilities such as BigBOSS, LSST, Euclid, and WFIRST. We briefly review a number of other approaches including redshift-space distortions, the Alcock-Paczynski effect, and direct measurements of the Hubble constant H 0 . We present extensive forecasts for constraints on the dark energy equation of state and parameterized deviations from General Relativity, achievable with Stage III and Stage IV experimental programs that incorporate supernovae, BAO, weak lensing, and cosmic microwave background data. We also show the level of precision required for clusters or other methods to provide constraints competitive with those of these fiducial programs. We emphasize the value of a balanced program that employs several of the most powerful methods in combination, both to cross-check systematic uncertainties and to take advantage of complementary information. Surveys to probe cosmic acceleration produce data sets that support a wide range of scientific investigations, and they continue the longstanding astronomical tradition of mapping the universe in ever greater detail over ever larger scales.

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Section: Figurementioning

confidence: 99%

Observational probes of cosmic acceleration

et al. 2013

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“…Campbell et al (2013) used SDSS ugriz light curves to classify 752 SNe as likely Type Ia, enough to measure the dark energy equation of state parameter, w, with ∼10% statistical uncertainty. Their sample was selected from light-curve properties and a classifier that compares each observed light curve to SN Ia and CC SN templates (PSNID; Sako et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Their final sample comprised just 3.9% CC SNe. While Campbell et al (2013) has the only SN Iabased measurement of w to date that does not use spectroscopic classification for its SNe, the measurement did not include systematic uncertainties. In addition, contaminating CC SNe bias their measurements of SN Ia dispersion and the correlation between SN luminosity and light-curve rise/decline rate by ∼60%.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

Jones

Scolnic

Riess

et al. 2017

ApJ

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The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,147 of these likely SNe and estimate that ∼1,000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, w. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined. We test light-curve based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on large SN simulations, we estimate that CC SN contamination gives a systematic error on w (σ CC w ) of 0.014, 29% of the statistical uncertainty. Our best method gives σ CC w = 0.004, just 8% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. This method determines the SALT2 color and shape coefficients, α and β, with ∼3% bias. However, we find that some variants require α and β to be fixed to known values for BEAMS to yield accurate measurements of w. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions.

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“…The SNANA simulations of SNe Ia have been heavily tested on SDSS photometric data up to z < 0.7 (see, e.g., Hlozek et al (2012);Campbell et al (2013)) , and on SNLS data up to z < 1.0. This is similar to the redshift range we are using.…”

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confidence: 99%

“…However, the observed photometric SNe need to be classified first using photometry (see "Supernova Photometric Classification Challenge" by Kessler et al (2010b) for a detailed discussion). Even applying an optimized version of the top performing method from the Supernova Photometric Classification Challenge, the photometric-classification algorithm of Sako et al (2011) result in over 25% of the resultant photometric SN Ia sample remaining non-Ia SNe (Campbell et al 2013). 3 We will defer the difficult task of photometric classificationo future work, and focus on the relatively easier task of estimating redshifts of known SNe Ia using photometry only.…”

Section: Introductionmentioning

confidence: 99%

Analytic photometric redshift estimator for Type Ia supernovae from the Large Synoptic Survey Telescope

Wang

Gjergo

Kuhlmann

2015

Monthly Notices of the Royal Astronomical Society

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Accurate and precise photometric redshifts (photo-z's) of Type Ia supernovae (SNe Ia) can enable the use of SNe Ia, measured only with photometry, to probe cosmology. This dramatically increases the science return of supernova surveys planned for the Large Synoptic Survey Telescope (LSST). In this paper we describe a significantly improved version of the simple analytic photo-z estimator proposed by Wang (2007) and further developed by Wang, Narayan, and Wood-Vasey (2007). We apply it to 55,422 simulated SNe Ia generated using the SNANA package with the LSST filters. We find that the estimated errors on the photo-z's, σ z phot /(1 + z phot ), can be used as filters to produce a set of photoz's that have high precision, accuracy, and purity. Using SN Ia colors as well as SN Ia peak magnitude in the i band, we obtain a set of photo-z's with 2 percent accuracy (with σ(z phot −z spec )/(1+z spec ) = 0.02), a bias in z phot (the mean of z phot −z spec ) of −9×10 −5 , and an outlier fraction (with |(z phot − z spec )/(1 + z spec )| > 0.1) of 0.23 percent, with the requirement that σ z phot /(1 + z phot ) < 0.01. Using the SN Ia colors only, we obtain a set of photo-z's with similar quality by requiring that σ z phot /(1 + z phot ) < 0.007; this leads to a set of photo-z's with 2 percent accuracy, a bias in z phot of 5.9 × 10 −4 , and an outlier fraction of 0.32 percent.

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COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY

Cited by 112 publications

References 131 publications

Observational probes of cosmic acceleration

Observational probes of cosmic acceleration

Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

Analytic photometric redshift estimator for Type Ia supernovae from the Large Synoptic Survey Telescope

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