Photometric Supernova Cosmology With Beams and SDSS-Ii

Hložek, Renée; Kunz, M.; Bassett, Bruce A.; Smith, M.; Newling, James; Varughese, Melvin; Kessler, R.; Bernstein, Joseph P.; Campbell, H.; Dilday, B.; Falck, Bridget; Frieman, Joshua A.; Kuhlmann, S.; Lampeitl, H.; Marriner, J.; Nichol, R. C.; Riess, Adam G.; Šako, M.; Schneider, Donald P.

doi:10.1088/0004-637x/752/2/79

Cited by 53 publications

(78 citation statements)

References 44 publications

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“…The first parameter is a scaling factor that corrects for globally skewed prior probabilities following Hlozek et al (2012). This normalization term allows BEAMS to correct for effects such Figure 7.…”

Section: Prior Probabilitiesmentioning

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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Section: Prior Probabilitiesmentioning

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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“…Therefore, 544 of our photometrically classified SNe Ia are have no spectroscopic information at all, comprising 72.2% of the sample. We note that only 115 of these 544 SNe Ia have been previously photometrically classified, using host-galaxy spectra from the SDSS-I/II surveys (S11; Hlozek et al 2012). The data for all SNe Ia in our final sample can be found in Appendix E.…”

Section: Hubble Diagrammentioning

confidence: 99%

“…This approach avoids the uncertainty of choosing the optimal P Ia threshold to obtain a "clean" sample of SNe Ia, and prevents the removal of actual SNe Ia and the information that they provide. These methods-the nearest-neighbor algorithm and the fully Bayesian method using the BEAMS algorithm (Hlozek et al 2012)-are currently being investigated using the SDSS-II SN candidates (and their BOSS host-galaxy redshifts).…”

Section: Future Improvements To Photometric Classificationmentioning

confidence: 99%

“…These methods (e.g., Poznanski et al 2002Poznanski et al , 2007Sullivan et al 2006;Johnson & Crotts 2006;Kuznetsova & Connolly 2007;Kunz et al 2007;Rodney & Tonry 2009;Gong et al 2010;Falck et al 2010;Sako et al 2011) typically remove SNe that resemble non-Ia SN templates based on their likelihoods, although there is considerable variety in the details. While Hlozek et al (2012) (based on Kunz et al 2007 and further developed by Newling et al 2011;Knights et al 2012) use templates to compute likelihoods for the type of each SN, they do not remove any objects from their cosmological fit. Rather, they use the likelihoods of each SN as a weight, retaining all possible information while computing an unbiased cosmology in a Bayesian manner.…”

Section: Introductionmentioning

confidence: 99%

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

Campbell

D’Andrea

Nichol

et al. 2013

ApJ

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113

128

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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 ΛCDM cosmological model, we find that our photometric sample alone gives Ω m = 0.24 +0.07 −0.05 (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on Ω m and Ω Λ , 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 , Ω m = 0.29 +0.02 −0.02 , and Ω k = 0.00 +0.03 −0.02 (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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“…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.…”

Section: Simulation Of Datamentioning

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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Photometric Supernova Cosmology With Beams and SDSS-Ii

Cited by 53 publications

References 44 publications

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

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

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

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

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