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We quantify the effect of supernova Type Ia peculiar velocities on the derivation of cosmological parameters. The published distant and local Ia SNe used for the Supernova Legacy Survey first-year cosmology report form the sample for this study. While previous work has assumed that the local SNe are at rest in the CMB frame (the No Flow assumption), we test this assumption by applying peculiar velocity corrections to the local SNe using three different flow models. The models are based on the IRAS PSCz galaxy redshift survey, have varying beta = Omega_m^0.6/b, and reproduce the Local Group motion in the CMB frame. These datasets are then fit for w, Omega_m, and Omega_Lambda using flatness or LambdaCDM and a BAO prior. The chi^2 statistic is used to examine the effect of the velocity corrections on the quality of the fits. The most favored model is the beta=0.5 model, which produces a fit significantly better than the No Flow assumption, consistent with previous peculiar velocity studies. By comparing the No Flow assumption with the favored models we derive the largest potential systematic error in w caused by ignoring peculiar velocities to be Delta w = +0.04. For Omega_Lambda, the potential error is Delta Omega_Lambda = -0.04 and for Omega_m, the potential error is Delta Omega_m < +0.01. The favored flow model (beta=0.5) produces the following cosmological parameters: w = -1.08 (+0.09,-0.08), Omega_m = 0.27 (+0.02,-0.02) assuming a flat cosmology, and Omega_Lambda = 0.80 (+0.08,-0.07) and Omega_m = 0.27 (+0.02,-0.02) for a w = -1 (LambdaCDM) cosmology.

We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is 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 Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). We challenge scientists to run their classification algorithms and report a type for each SN. A spectroscopically confirmed subset is provided for training. The goals of this challenge are to (1) learn the relative strengths and weaknesses of the different classification algorithms, (2) use the results to improve classification algorithms, and (3) understand what spectroscopically confirmed sub-sets are needed to properly train these algorithms. The challenge is available at www.hep.anl.gov/SNchallenge, and the due date for classifications is May 1, 2010.

Recent studies indicate that Type Ia supernovae (SNe Ia) consist of two groups - a \"prompt\" component whose rates are proportional to the host galaxy star formation rate, whose members have broader lightcurves and are intrinsically more luminous, and a \"delayed\" component whose members take several Gyr to explode, have narrower lightcurves, and are intrinsically fainter. As cosmic star formation density increases with redshift, the prompt component should begin to dominate. We use a two-component model to predict that the average lightcurve width should increase by 6% from z=0-1.5. Using data from various searches we find an 8.1% +/- 2.7% increase in average lightcurve width for non-subluminous SNe Ia from z=0.03 - 1.12, corresponding to an increase in the average intrinsic luminosity of 12%. To test whether there is any bias after supernovae are corrected for lightcurve shape we use published data to mimic the effect of population evolution and find no significant difference in the measured dark energy equation of state parameter, w. However, future measurements of time-variable w will require standardization of SN Ia magnitudes to 2% up to z=1.7, and it is not yet possible to assess whether lightcurve shape correction works at this level of precision. Another concern at z=1.5 is the expected order of magnitude increase in the number of SNe Ia that cannot be calibrated by current methods.

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. 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 9 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 SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.

We use multi-wavelength, matched aperture, integrated photometry from GALEX, SDSS and the RC3 to estimate the physical properties of 166 nearby galaxies hosting 168 well-observed Type Ia supernovae (SNe Ia). Our data corroborate well-known features that have been seen in other SN Ia samples. Specifically, hosts with active star formation produce brighter and slower SNe Ia on average, and hosts with luminosity-weighted ages older than 1 Gyr produce on average more faint, fast and fewer bright, slow SNe Ia than younger hosts. New results include that in our sample, the faintest and fastest SNe Ia occur only in galaxies exceeding a stellar mass threshhold of ~10^10 M_sun, indicating that their progenitors must arise in populations that are older and/or more metal rich than the general SN Ia population. A low host extinction sub-sample hints at a residual trend in peak luminosity with host age, after correcting for light-curve shape, giving the appearance that older hosts produce less-extincted SNe Ia on average. This has implications for cosmological fitting of SNe Ia and suggests that host age could be useful as a parameter in the fitting. Converting host mass to metallicity and computing 56Ni mass from the supernova light curves, we find that our local sample is consistent with a model that predicts a shallow trend between stellar metallicity and the 56Ni mass that powers the explosion, but we cannot rule out the absence of a trend. We measure a correlation between 56Ni mass and host age in the local universe that is shallower and not as significant as that seen at higher redshifts. The details of the age -- 56Ni mass correlations at low and higher redshift imply a luminosity-weighted age threshhold of ~3 Gyr for SN Ia hosts, above which they are less likely to produce SNe Ia with 56Ni masses above ~0.5 M_sun. (Abridged)