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

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          Cosmological parameters from CMB and other data: a Monte-Carlo approach

          We present a fast Markov Chain Monte-Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent CMB experiments and provide parameter constraints, including sigma_8, from the CMB independent of other data. We next combine data from the CMB, HST Key Project, 2dF galaxy redshift survey, supernovae Ia and big-bang nucleosynthesis. The Monte Carlo method allows the rapid investigation of a large number of parameters, and we present results from 6 and 9 parameter analyses of flat models, and an 11 parameter analysis of non-flat models. Our results include constraints on the neutrino mass (m_nu < 0.3eV), equation of state of the dark energy, and the tensor amplitude, as well as demonstrating the effect of additional parameters on the base parameter constraints. In a series of appendices we describe the many uses of importance sampling, including computing results from new data and accuracy correction of results generated from an approximate method. We also discuss the different ways of converting parameter samples to parameter constraints, the effect of the prior, assess the goodness of fit and consistency, and describe the use of analytic marginalization over normalization parameters.
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            Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

            We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The data set includes several low-redshift samples (z<0.1), all 3 seasons from the SDSS-II (0.05 < z < 0.4), and 3 years from SNLS (0.2
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              Nearby Supernova Rates from the Lick Observatory Supernova Search. II. The Observed Luminosity Functions and Fractions of Supernovae in a Complete Sample

              This is the second paper of a series in which we present new measurements of the observed rates of supernovae (SNe) in the local Universe, determined from the Lick Observatory Supernova Search (LOSS). In this paper, a complete SN sample is constructed, and the observed (uncorrected for host-galaxy extinction) luminosity functions (LFs) of SNe are derived. These LFs solve two issues that have plagued previous rate calculations for nearby SNe: the luminosity distribution of SNe and the host-galaxy extinction. We select a volume-limited sample of 175 SNe, collect photometry for every object, and fit a family of light curves to constrain the peak magnitudes and light-curve shapes. The volume-limited LFs show that they are not well represented by a Gaussian distribution. There are notable differences in the LFs for galaxies of different Hubble types (especially for SNe Ia). We derive the observed fractions for the different subclasses in a complete SN sample, and find significant fractions of SNe II-L (10%), IIb (12%), and IIn (9%) in the SN II sample. Furthermore, we derive the LFs and the observed fractions of different SN subclasses in a magnitude-limited survey with different observation intervals, and find that the LFs are enhanced at the high-luminosity end and appear more "standard" with smaller scatter, and that the LFs and fractions of SNe do not change significantly when the observation interval is shorter than 10 days. We also discuss the LFs in different galaxy sizes and inclinations, and for different SN subclasses. Some notable results are ... (abridged).
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                Author and article information

                Journal
                The Astrophysical Journal
                ApJ
                American Astronomical Society
                1538-4357
                July 01 2017
                June 26 2017
                : 843
                : 1
                : 6
                Article
                10.3847/1538-4357/aa767b
                e527caff-fceb-475c-b088-44e64b9b93ec
                © 2017

                http://iopscience.iop.org/info/page/text-and-data-mining

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