We report measurements of the mass density, Ω M , and cosmological-constant energy density, Ω Λ , of the universe based on the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology Project. The magnitude-redshift data for these supernovae, at redshifts between 0.18 and 0.83, are fit jointly with a set of supernovae from the Calán/Tololo Supernova Survey, at redshifts below 0.1, to yield values for the cosmological parameters. All supernova peak magnitudes are standardized using a SN Ia lightcurve width-luminosity relation. The measurement yields a joint probability distribution of the cosmological parameters that is approximated by the relation 0.8 Ω M − 0.6 Ω Λ ≈ −0.2 ± 0.1 in the region of interest (Ω M ∼ < 1.5). For a flat (Ω M + Ω Λ = 1) cosmology we find Ω flat M = 0.28 +0.09 −0.08 (1σ statistical) +0.05 −0.04 (identified systematics). The data are strongly inconsistent with a Λ = 0 flat cosmology, the simplest inflationary universe model. An open, Λ = 0 cosmology also does not fit the data well: the data indicate that the cosmological constant is non-zero and positive, with a confidence of P(Λ > 0) = 99%, including the identified systematic uncertainties. The best-fit age of the universe relative to the Hubble time is t flat 0 = 14.9 +1.4 −1.1 (0.63/h) Gyr for a flat cosmology. The size of our sample allows us to perform a variety of statistical tests to check for possible systematic errors and biases. We find no significant differences in either the host reddening distribution or Malmquist bias between the low-redshift Calán/Tololo sample and our high-redshift sample. Excluding those few supernovae which are outliers in color excess or fit residual does not significantly change the results. The conclusions are also robust whether or not a width-luminosity relation is used to standardize the supernova peak magnitudes. We discuss, and constrain where possible, hypothetical alternatives to a cosmological constant.

The ultimate fate of the universe, infinite expansion or a big crunch, can be determined by measuring the redshifts, apparent brightnesses, and intrinsic luminosities of very distant supernovae. Recent developments have provided tools that make such a program practicable: (1) Studies of relatively nearby

We report on work to increase the number of well-measured Type Ia supernovae (SNe Ia) at high redshifts. Light curves, including high signal-to-noise HST data, and spectra of six SNe Ia that were discovered during 2001 are presented. Additionally, for the two SNe with z > 1, we present groundbased J-band photometry from Gemini and the VLT. These are among the most distant SNe Ia for which ground based near-IR observations have been obtained. We add these six SNe Ia together with other data sets that have recently become available in the literature to the Union compilation (Kowalski et al. 2008). We have made a number of refinements to the Union analysis chain, the most important ones being the refitting of all light curves with the SALT2 fitter and an improved handling of systematic errors. We call this new compilation, consisting of 557 supernovae, the Union2 compilation. The flat concordance ΛCDM model remains an excellent fit to the Union2 data with the best fit constant equation of state parameter w = −0.997 +0.050 −0.054 (stat)+0.077 −0.082 (stat + sys together) for a flat universe, or w = −1.035 +0.055 −0.059 (stat)+0.093 −0.097 (stat + sys together) with curvature. We also present improved constraints on w(z). While no significant change in w with redshift is detected, there is still considerable room for evolution in w. The strength of the constraints depend strongly on redshift. In particular, at z 1, the existence and nature of dark energy are only weakly constrained by the data.

We present ACS, NICMOS, and Keck AO-assisted photometry of 20 Type Ia supernovae (SNe Ia) from the HST Cluster Supernova Survey. The SNe Ia were discovered over the redshift interval 0.623 < z < 1.415. Fourteen of these SNe Ia pass our strict selection cuts and are used in combination with the world's sample of SNe Ia to derive the best current constraints on dark energy. Ten of our new SNe Ia are beyond redshift z = 1, thereby nearly doubling the statistical weight of HST-discovered SNe Ia beyond this redshift. Our detailed analysis corrects for the recently identified correlation between SN Ia luminosity and host galaxy mass and corrects the NICMOS zeropoint at the count rates appropriate for very distant SNe Ia. Adding these supernovae improves the best combined constraint on dark energy density, ρ DE (z), at redshifts 1.0 < z < 1.6 by 18% (including systematic errors). For a flat ΛCDM universe, we find Ω Λ = 0.729 +0.014 −0.014 (68% CL including systematic errors). For a flat wCDM model, we measure a constant dark energy equation-of-state parameter w = −1.013 +0.068 −0.073 (68% CL). Curvature is constrained to ∼ 0.7% in the owCDM model and to ∼ 2% in a model in which dark energy is allowed to vary with parameters w 0 and w a . Tightening further the constraints on the time evolution of dark energy will require several improvements, including high-quality multi-passband photometry of a sample of several dozen z > 1 SNe Ia. We describe how such a sample could be efficiently obtained by targeting cluster fields with WFC3 on HST.The updated supernova Union2.1 compilation of 580 SNe is available at http://supernova.lbl.gov/Union ⋆ is less than the mass threshold. We begin by noting that.We can then integrate this probability over all true host masses less than the threshold:⋆ )P (m true ⋆ ) up to a normalization constant found by requiring the integral to be unity when integrating over all possible true masses. P (m true ⋆ ) is estimated from the observed distribution for each type of survey. The SNLS (Sullivan et al. 2010) and SDSS (Lampeitl et al. 2010) host masses were assumed to be representative of untargeted surveys, while the mass distribution in Kelly et al. (2010) was assumed typical of nearby targeted surveys. As these distributions are approximately log-normal, we use this model for P (m true ⋆) using the mean and RMS from the log of the host masses from these surveys (with the average measurement errors subtracted in quadrature), giving log 10 P (m true ⋆ ) = N (µ = 9.88, σ 2 = 0.92 2 ) for untargeted surveys and log 10 P (m true ⋆ ) = N (10.75, 0.66 2 ) for targeted surveys. When host mass measurements are available, P (m obs ⋆ |m true ⋆ ) is also modeled as a log-normal; when no measurement is available, a flat distribution is used.For a supernova from an untargeted survey with no host mass measurement (including supernovae presented in this paper which are not in a cluster), P (m trueis the integral of P (m true ⋆ ) up to the threshold mass: 0.55. Similarly, nearby supernovae from targeted surveys w...