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The Astrophysical Journal, 666:694 Y715, 2007 September 10
# 2007. The American Astronomical Society. All rights reserved. Printed in U.S.A.

A

OBSERVATIONAL CONSTRAINTS ON THE NATURE OF DARK ENERGY: FIRST COSMOLOGICAL RESULTS FROM THE ESSENCE SUPERNOVA SURVEY
W. M. Wood-Vasey,1 G. Miknaitis,2 C. W. Stubbs,1,3 S. Jha,4,5 A. G. Riess,6,7 P. M. Garnavich, R. P. Kirshner,1 C. Aguilera,9 A. C. Becker,10 J. W. Blackman,11 S. Blondin,1 P. Challis,1 A. Clocchiatti,12 A. Conley,13 R. Covarrubias,10 T. M. Davis,14 A. V. Filippenko,4 R. J. Foley,4 A. Garg,1,3 M. Hicken,1,3 K. Krisciunas,8,15 B. Leibundgut,16 W. Li,4 T. Matheson,17 A. Miceli,10 G. Narayan,1,3 G. Pignata,12 J. L. Prieto,18 A. Rest,9 M. E. Salvo,11 B. P. Schmidt,11 R. C. Smith,9 J. Sollerman,14,19 J. Spyromilio,16 J. L. Tonry,20 N. B. Suntzeff,9,15 and A. Zenteno9
Received 2006 November 21; accepted 2007 April 2
8

ABSTRACT We present constraints on the dark energy equation-of-state parameter, w ¼ P/( c 2 ), using60SNe Ia from the ESSENCE supernova survey. We derive a set of constraints on the nature of the dark energy assuming a flat universe. By including constraints on (M, w) from baryon acoustic oscillations, we obtain a value for a static equation-of-state parameter w ¼ þ1:05×0::13 (stat 1 ) ô 0:13 (sys) and M ¼ 0:274×0::033 (stat 1 ) with a bestþ0 020 þ0 12 fit 2/dof of 0.96. These results are consistent with those reported by the Supernova Legacy Survey from the first year of a similar program measuring supernova distances and redshifts. We evaluate sources of systematic error that afflict supernova observations and present Monte Carlo simulations that explore these effects. Currently, the largest systematic with the potential to affect our measurements is the treatment of extinction due to dust in the supernova host galaxies. Combining our set of ESSENCE SNe Ia with the first-results Supernova Legacy Survey SNe Ia, we obtain a joint constraint of w ¼ þ1:07×0::09 (stat 1 ) ô 0:13 (sys), M ¼ 0:267×0::028 (stat 1 ) with þ0 09 þ0 018 abest-fit 2/dof of 0.91. The current global SN Ia data alone rule out empty (M ¼ 0), matter-only M ¼ 0:3, and M ¼ 1 universes at >4.5 . The current SN Ia data are fully consistent with a cosmological constant. Subject headings: cosmological parameters -- cosmology: observations -- supernovae: general Online material: color figures

1. INTRODUCTION: SUPERNOVAE AND COSMOLOGY We report on the analysis of 60 Type Ia supernovae (SNe Ia) discovered in the course of the ESSENCE program ( Equation of State: SupErNovae trace Cosmic Expansion; an NOAO Survey Program) from 2002 through 2005. The aim of ESSENCE is to measure the history of cosmic expansion over the past 5 billion years with sufficient precision to distinguish whether the dark energy is different from a cosmological constant at the w ¼ ô 0:1 level. Here we present our first results and show that we are well on our way toward that goal. Our present data are fully consistent with a w ¼ þ1, flat universe, and our uncertainty in w, the parameter that describes the cosmic equation
1 Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138; wmwood-vasey@cfa.harvard.edu. 2 Fermilab, Batavia, IL 60510-0500. 3 Department of Physics, Harvard University, Cambridge, MA 02138. 4 Department of Astronomy, University of California , Berkeley, CA 94720-3411. 5 Kavli Institute for Particle Astrophysics and Cosmology, Stanford Linear Accelerator Center, MS 29, Menlo Park, CA 94025. 6 Space Telescope Science Institute, Baltimore, MD 21218. 7 Johns Hopkins University, Baltimore, MD 21218. 8 Department of Physics, University of Notre Dame, Notre Dame, IN 46556-5670. 9 Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, Casilla 603, La Serena, Chile. 10 Department of Astronomy, University of Washington, Seattle, WA 981951580. 11 Research School of Astronomy and Astrophysics, Australian National University, Mount Stromlo and Siding Spring Observatories, Weston Creek PO 2611, Australia.

of state, analyzed in the way we outline here, will shrink below 0.1 for models of constant w as the ESSENCE program is completed. Other approaches to using the luminosity distances have been suggested to constrain possible cosmological models. We here provide the ESSENCE observations in a convenient form suitable for testing a variety of models.21 As reported in a companion paper ( Miknaitis et al. 2007), ESSENCE is based on a supernova search carried out with the 4 m Blanco Telescope at the Cerro Tololo Inter-American Observatory (CTIO) with the prime-focus MOSAIC II 64 Megapixel CCD camera. Our search produces densely sampled R-band and I-band light curves for supernovae in our fields. As described by Miknaitis et al. (2007), we optimized the search to provide the best constraints on w, given fixed observing time and the properties of both the MOSAIC II camera and the CTIO 4 m
12 ´ Pontificia Universidad Catolica de Chile, Departamento de Astronom´a y i Astrof´sica, Casilla 306, Santiago 22, Chile. i 13 Department of Astronomy and Astrophysics, University of Toronto, Toronto, ON M5S 3H4, Canada. 14 Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, DK-2100 Copenhagen , Denmark. 15 Department of Physics, Texas A&M University, College Station, TX 77843- 4242. 16 European Southern Observatory, D-85748 Garching, Germany. 17 National Optical Astronomy Observatory, Tucson, AZ 85719- 4933. 18 Department of Astronomy, Ohio State University, Columbus, OH 43210. 19 Department of Astronomy, Stockholm University, AlbaNova, 10691 Stockholm, Sweden. 20 Institute for Astronomy, University of Hawaii, Honolulu, HI 96822. 21 See http:// www.ctio.noao.edu /essence /.

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DARK ENERGY FROM ESSENCE SURVEY telescope. Spectra from a variety of large telescopes, including Keck, VLT, Gemini, and Magellan, allow us to determine supernova types and redshifts. We have paid particular attention to the central problems of calibration and systematic errors that, on completion of the survey in 2008, will be more important to the final precision of our cosmological inferences than statistical sampling errors for about 200 objects. This first cosmological report from the ESSENCE survey derives some properties of dark energy from the sample presently in hand, which is still small enough that the statistics of the sample size make a noticeable contribution to the uncertainty in dark energy properties. But our goal is to set out the systematic uncertainties in a clear way so that these are exposed to view and so that we can concentrate our efforts where they will have the most significant effect. To infer luminosity distances to the ESSENCE supernovae over the redshift interval 0.15Y0.70, we employ the relations developed for SNe Ia at low redshift (Jha et al. 2007 and references therein) among their light-curve shapes, colors, and intrinsic luminosities. The expansion history from z % 0:7tothe present provides leverage to constrain the equation-of-state parameter for the dark energy as described below. In x 1 we sketch the context of the ESSENCE program. In x 2 we show from a set of simulated light curves that this particular implementation of light-curve analysis is consistent, with the same cosmology emerging from the analysis as was used to construct the samples, and that the statistical uncertainty we ascribe to the inference of the dark energy properties is also correctly measured. This modeling of our analysis chain gives us confidence that the analysis of the actual data set is reliable and its uncertainty is correctly estimated. Section 3 delineates the systematic errors we confront, estimates their present size, and indicates some areas where improvement can be achieved. Section 4 describes the sample and provides the estimates of dark energy properties using the ESSENCE sample. The conclusions of this work are given in x 5. 1.1. Context Supernovae have been central to cosmological measurements from the very beginning of observational cosmology. Shapley (1919) employed supernovae against the ``island universe'' hypothesis, arguing that objects such as SN 1885A in the Andromeda Nebula would have M ¼ þ16 mag, which was ``out of the question.'' Edwin Hubble noted ``a mysterious class of exceptional novae which attain luminosities that are respectable fractions of the total luminosities of the systems in which they appear '' ( Hubble 1929). These extrabright novae were dubbed ``supernovae'' by Baade & Zwicky (1934). Minkowski (1941) divided them into two classes based on their spectra: Type I supernovae (SNe I ) have no hydrogen lines, while Type II supernovae (SNe II ) show H and other hydrogen lines. The high luminosity and observed homogeneity of the first handful of SN I light curves prompted Wilson (1939) to suggest that they be employed for fundamental cosmological measurements, starting with time dilation of their characteristic rise and fall to distinguish true cosmic expansion from ``tired light.'' After the SN Ib and SN Ic subclasses were separated from the SNe Ia ( for a review see Filippenko 1997), this line of investigation has grown more fruitful as techniques of photometry have improved and as the redshift range over which supernovae have been well observed and confirmed to have standard light-curve shapes and luminosities has increased ( Rust 1974; Leibundgut et al. 1996; Riess et al. 1997, 2004; Goldhaber et al. 2001; Foley et al. 2005; Hook et al. 2005; Conley et al. 2006; Blondin et al. 2006). Within the uncertainties, the results agree with the predictions of cosmic

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expansion and provide a fundamental test that the underlying assumption of an expanding universe is correct. Evidence for the homogeneity of SNe Ia comes from their small scatter in the Hubble diagram. Kowal (1968) compiled data for the first well-populated Hubble diagram of SNe I. The 1 scatter about the Hubble law was 0.6 mag, but Kowal presciently speculated that distances to individual supernovae might eventually be known to 5% Y10% and suggested that ``[i]t may even be possible to determine the second-order term in the redshiftmagnitude relation when light curves become available for very distant supernovae.'' Precise distances to SNe Ia enable tests for the linearity of the Hubble law and provide evidence for local deviations from the local Hubble flow, attributed to density inhomogeneities in the local universe ( Riess et al. 1995, 1997; Zehavi et al. 1998; Bonacic et al. 2000; Radburn-Smith et al. 2004; Jha et al. 2007). While SN Ia cosmology is not dependent on the value of H0 ,it is sensitive to deviations from a homogeneous Hubble flow, and these regional velocity fields may limit our ability to estimate properties of dark energy, as emphasized by Hui & Greene (2006) and Cooray & Caldwell (2006). Whether the best strategy is to map the velocity inhomogeneities thoroughly or to skip over them by using a more distant low-redshift sample remains to be demonstrated. We have used a lower limit of redshift z > 0:015 in constructing our sample of SNe Ia. The utility of SNe Ia as distance indicators results from the demonstration that the intrinsic brightness of each SN Ia is closely connected to the shape of its light curve. As the sample of wellobserved SNe Ia grew, some distinctly bright and faint objects were found. For example, SN 1991T ( Filippenko et al. 1992b; Phillips et al. 1992) and SN 1991bg ( Filippenko et al. 1992a; Leibundgut et al. 1993) were of different luminosity, and their light curves were not the same either. The possible correlation of the shapes of supernova light curves with their luminosities had been explored by Pskovskii (1977). More homogeneous photometry from CCD detectors, more extreme examples from larger samples, and more reliable distance estimators enabled Phillips (1993) to establish the empirical relation between light´ curve shapes and supernova luminosities. The Calan-Tololo sample ( Hamuy et al. 1996) and the CfA sample ( Riess et al. 1999; Jha et al. 2006b) of SNe Ia have been used to improve the methods for using supernova light curves to measure supernova distances. Many variations on Phillips's idea have been developed, including à m15 ( Phillips et al. 1999), MLCS ( Riess et al. 1996; Jha et al. 2007), DM15 ( Prieto et al. 2006), stretch (Goldhaber et al. 2001), CMAGIC ( Wang et al. 2003), and SALT (Guy et al. 2005, 2007). These methods are capable of achieving the 10% precision for supernova distances that Kowal (1968) foresaw 40 years ago. In the ESSENCE analysis, we have used a version of the Jha et al. (2007) method called MLCS2k2. We have compared it with the results of the Spectral Adaptive Lightcurve Template (SALT; Guy et al. 2005) light-curve fitter used by the SNLS (Astier et al. 2006, hereafter A06). This comparison provides a test: if the two approaches do not agree when applied to the same data, they cannot both be correct. As shown in x 2, SALT and this version of MLCS2k2, with our preferred extinction prior, are in excellent accord when applied to the same data. While gratifying, this agreement does not prove that they are both correct. Moreover, as described in x 4, the cosmological results depend somewhat on the assumptions about SN host galaxy extinction that are employed. This has been an ongoing problem in supernova cosmology. The work of Lira (1995) demonstrated the empirical fact that although SNe Ia have a range of colors at


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maximum light, they appear to reach the same intrinsic color about 30Y90 days past maximum light, independent of lightcurve shape. Riess et al. (1996) used dereddened SN Ia data to show that intrinsic color differences exist near maximum light, with fainter SNe Ia appearing redder than brighter objects, and then used this information to construct an absorption-free Hubble diagram. Given a good set of observations in several bands, the reddening for individual supernovae can then be determined and the general relations between supernova luminosity and the light-curve shapes in many bands can be established ( Hamuy et al. 1996; Riess et al. 1999; Phillips et al. 1999). The initial detections of cosmic acceleration employed either these individual absorption corrections ( Riess et al. 1998) or a full-sample statistical absorption correction ( Perlmutter et al. 1999). Finding the best approach to this problem, whether by shifting observations to the infrared, limiting the sample to low-extinction cases, or making other restrictive cuts on the data, is an important area for future work. Some ways to explore this issue are sketched in x 4. Kowal (1968) recognized that second-order terms in cosmic expansion might be measured with supernovae once the precision and redshift range grew sufficiently large. More direct approaches with the Hubble Space Telescope (HST ) were imagined by Colgate (1979) and Tammann (1979). Tammann anticipated that HST photometry of SNe Ia at z % 0:5 would lead to a direct determination of cosmic deceleration and that the time dilation of SN Ia light curves would be a fundamental test of the expansion hypothesis. Early attempts at high-redshift supernova detection were undertaken by a Danish group in 1986Y1988 through the European Southern Observatory ( ESO) 1.5 m telescope at La Silla Observatory. Their cyclic CCD imaging of the search fields used image registration, convolution and subtraction, and realtime data analysis ( Hansen et al. 1987). Alas, the rate of SNe Ia in their fields was lower than they had anticipated, and only one SN Ia, SN 1988U, was discovered and monitored in 2 years of effort ( Hansen et al. 1987; Norgaard-Nielsen et al. 1989). More effective searches by the Lawrence Berkeley National Laboratory ( LBNL) group exploiting larger CCD detectors and sophisticated detection software showed that this approach could be made practical and be used to find significant numbers of highredshift SNe Ia ( Perlmutter et al. 1995). By 1995, two groups, the LBNL-based Supernova Cosmology Project (SCP) and the High-Z Supernova Search Team ( HZT; Schmidt et al. 1998), were working in this field. The first SN Ia cosmology results using seven high-redshift SNe Ia ( Perlmutter et al. 1997) found a universe consistent with M ¼ 1, but subsequent work by the SCP ( Perlmutter et al. 1998) and by the HZT (Garnavich et al. 1998) revised this initial finding to favor a lower value of M. At the 1998 January meeting of the American Astronomical Society both teams reported that the SN Ia results favored a universe that would expand without limit, but at that time neither team claimed that the universe was accelerating. The subsequent publication of stronger results based on larger samples by the HZT ( Riess et al. 1998) and by the SCP ( Perlmutter et al. 1999) provided a surprise. The supernova data showed that SNe Ia at z % 0:5 were about 0.2 mag dimmer than expected in an open universe and pointed firmly at an accelerating universe ( for first-hand accounts, see Overbye 1999, p. 426; Riess 2000; Filippenko 2001; Kirshner 2002; Perlmutter 2003; reviews are given by, e.g., Leibundgut 2001; Filippenko 2004, 2005b). The supernova route to cosmological understanding continues to improve. One source of uncertainty has been the small sample of very well observed low-redshift supernovae ( Hamuy et al. 1996; Riess et al. 1999). The most recent contribution is the

summary of CfA data obtained in 1997Y2001 (Jha et al. 2006b), but significantly enhanced samples from the CfA ( Hicken et al. 2006), the Katzman Automatic Imaging Telescope ( KAIT; Li et al. 2000; Filippenko et al. 2001; Filippenko 2005a), the Carnegie SN Program ( Hamuy et al. 2006), the Nearby Supernova Factory ( Wood-Vasey et al. 2004; Copin et al. 2006), and the Sloan Digital Sky Survey II Supernova Survey (SDSS-II; Frieman et al. 2004; Dilday et al. 2005) are forthcoming. As the low-z sample approaches 200 objects, the size of the sample will cease to be a source of statistical uncertainty for the determination of cosmological parameters. As described in x 3, systematic errors of calibration and K-correction will ultimately impose the limits to understanding dark energy's properties, and we are actively working to improve these areas (Stubbs & Tonry 2006). Some of the potential sources of systematic error in the high-z sample have been examined. The fundamental assumption is that distant SNe Ia can be analyzed using the methods developed for the low-z sample. Since nearby samples show that the SNe Ia in elliptical galaxies have a different distribution in luminosity than the SNe Ia in spirals ( Hamuy et al. 2000; Howell 2001; Gallagher et al. 2005; Neill et al. 2006; Sullivan et al. 2006b), morphological classification of the distant sample may provide some useful clues to help improve the cosmological inferences ( Williams et al. 2003). For example, Sullivan et al. (2003) showed that restricting the SCP sample to SNe Ia in elliptical galaxies gave identical cosmological results to the complete sample, which is principally in spiral galaxies. The possibility of gray dust raised by Aguirre (1999a, 1999b) was examined by Riess et al. (2000) and Nobili et al. (2005) through infrared observations of high-z supernovae and was put to rest by the very high redshift observations of Riess et al. (2004, 2007). Improved methods for handling the vexing problems of absorption by dust have been developed by Knop et al. (2003) and Jha et al. (2007). These questions are described in more detail in x 3.3. The question of whether distant supernovae have spectra that are the same as those of nearby supernovae has been investigated by Coil et al. (2000), Lidman et al. (2005), Matheson et al. (2005), Hook et al. (2005), Howell et al. (2005), and Blondin et al. (2006). Foley et al. (2005) then confirmed that spectra of distant SNe Ia evolve over the lifetime of the SN Ia in the same way as those of nearby SNe Ia. In all cases, the evidence points toward nearby SNe Ia behaving in the same way as distant ones, bolstering confidence in the initial results. This observed consistency does not mean that the samples are identical, only that the variations between the nearby and distant samples are successfully accounted for by the methods currently in hand. We do not know whether this will continue to be the case as future investigations press for more stringent limits on cosmological parameters (Albrecht et al. 2006). The highest redshift SN Ia data ( Riess et al. 2004, 2007) show the qualitative signature expected from a mixed dark energy/dark matter cosmology. Specifically, they show that cosmic deceleration due to dark matter preceded the current era of cosmic acceleration produced by dark energy. The sign of the observed effect on supernova apparent magnitudes reverses: SNe Ia at z % 0:5 appear 0.2 mag dimmer than expected in a coasting cosmology, but the very distant supernovae whose light comes from z > 1 appear brighter than they would in that cosmology. By itself, this turnover is a very encouraging sign that supernova cosmology does not founder on gray dust or even on a simple evolution of supernova properties with cosmic epoch. As part of this analysis, Riess et al. (2004) constructed the ``gold'' sample of high-z and low-z supernovae whose observations met reasonable criteria for inclusion in an analysis of all of the published light curves and


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spectra using a uniform method of deriving distances from the light curves. The analysis of the gold sample provided an estimate of the time derivative of the equation-of-state parameter, w, for dark energy. These observations are very important conceptually because the simplest fact about the cosmological constant as a candidate for dark energy is that it should be constant with redshift (i.e., w 0 ¼ dw/dz ¼ 0). The observations are consistent with a constant dark energy over the redshift range out to z % 1:6. Other forms of dark energy might satisfy the observed constraints, but this observational test is one that the cosmological constant could have failed. The next definitive advance in our understanding of w came from the SNLS analysis of 71 SNe Ia, which constrained constant models of w to w ¼ þ1:023 ô 0:09 (stat) ô 0:054 (sys) and was consistent with a flat universe dominated by a cosmological constant (A06). In the analysis of the ESSENCE data presented in x 4, we use the supernova data to constrain the properties of w, as first carried out by White (1998) and Garnavich et al. (1998). This parameterization of dark energy by w is not the only possible approach. A more detailed approach is to compare the observational data to a specific model and, for example, try to reconstruct the dark energy scalar field potential (see, e.g., Li et al. 2007). A more agnostic view is that we are simply measuring the expansion history of the universe, and a kinematic description of that history in terms of expansion rate, acceleration, and jerk ( Riess et al. 2004, 2007; Rapetti et al. 2007) covers the facts without assuming anything about the nature of dark energy. The ESSENCE project was conceived to tighten the constraints on dark energy at z % 0:5 to reveal any discrepancy between the observations and the leading candidate for dark energy, the cosmological constant. A simple way to express this is that we aim for a 10% uncertainty in the value of w. This program is similar to the approach of the SNLS being carried out at the Canada-France-Hawaii Telescope, and we compare our methods and results to theirs (Guy et al. 2005; A06) at several points in the analysis below. The SNLS has taken the admirable step of publishing their light curves online and making the code of their light-curve fitting program, SALT, available for public inspection and use.22 Making the light curves public, as was done for the results of the HZT and its successors ( Riess et al. 1998; Tonry et al. 2003; Barris et al. 2004; Krisciunas et al. 2005; Clocchiatti et al. 2006), by Knop et al. (2003), by Riess et al. (2004, 2007) for the very high redshift HST supernova program, and for the low-z data of Hamuy et al. (1996), Riess et al. (1999), and Jha et al. (2006b), provides the opportunity for others to perform their own analysis of the results. In addition to exploring a variety of approaches to analyzing our own SN Ia observations, in x 4 we show the first joint constraints from ESSENCE and the first year of SNLS, as well as some joint constraints derived from combining these with the Riess et al. (2004) gold sample. 2. LUMINOSITY DISTANCE DETERMINATION The physical quantities of interest in our cosmological measurements are the redshifts and distances to a set of spacetime points in the universe. The redshifts come from spectra and the luminosity distances, DL , come from the observed flux of the supernova combined with our understanding of SN Ia light curves from nearby objects. Extracting a luminosity distance to a supernova from observations of its light curve necessitates a number of assumptions.
22

We use the observations of nearby SNe Ia to establish the relations between color, light-curve shape in multiple bands, and peak luminosity. These nearby observations attain high signalto-noise ratios (S/ Ns), and the nearby objects can be observed in more passbands (including infrared) than faint, distant objects. We assume that the resulting method of converting light curves to luminosity distances applies at all redshifts. The observed spectral uniformity of supernovae over a range of redshift (Coil et al. 2000; Lidman et al. 2005; Hook et al. 2005; Blondin et al. 2006) supports this approach. We assume that RV , the ratio of extinction in the V band to the color excess E (B þ V ), is independent of redshift. In x 3.3 we test the potential systematic effect of departures from this assumption. We adopt an astrophysically sensible prior distribution of host galaxy extinction properties, with a redshift dependence that is derived from the simulations we present below. Our approach is to conduct comprehensive simulations of the ESSENCE data and analysis. As described by Miknaitis et al. (2007), we use this same approach to explore our photometric performance. For the aspects of our analysis that are ``downstream'' of the light-curve generation, we generate sets of synthetic light curves and subject them to our analysis pipeline. In this way we can test the performance of our distance-fitting tools, and by exaggerating various systematic errors (zero-point offsets, etc.), we can assess the impact of these effects on our determination of w. 2.1. Extracting Luminosity Distances from Light Curves: Distance Fitters We use the MLCS2k2 method of Jha et al. (2007) as the primary tool to derive relative luminosity distances to our SNe Ia. For comparison, we also provide the results obtained using the SALT fitter of Guy et al. (2005) on the ESSENCE light curves. SALT was used in the recent cosmological results paper from the SNLS (A06). We provide a consistent and comprehensive set of distances obtained to nearby, ESSENCE, and SNLS supernovae for each luminosity distance fitting technique. The ESSENCE light curves used in this analysis were presented by Miknaitis et al. (2007), and we provide them online, together with our set of previously published light curves for nearby SNe Ia, for the convenience of those interested (see footnote 21). Additional SN Ia light-curve fitting methods will be further explored in future ESSENCE analyses. Understanding the behavior of our distance determination method is critical to our goal of quantifying the uncertainties of our analysis chain. MLCS2k2 and SALT, as well as the light-curve ``stretch'' approach used by Perlmutter et al. (1997, 1999), Goldhaber et al. (2001), and Knop et al. (2003), exploit the fact that the rate of decline, the color, and the intrinsic luminosity of SNe Ia are correlated. At present we treat SNe Ia as a single-parameter family, and the distance-fitting techniques use multicolor light curves to deduce a luminosity distance and host galaxy reddening for each supernova. Previous papers have shown that the different techniques produce relative luminosity distances that scatter by $0.10 mag for an individual SN Ia (e.g., Tonry et al. 2003), but this scatter is uncorrelated with redshift. Consequently, the cosmological results are insensitive to the distance fitting technique. However, as described by Miknaitis et al. (2007), the measurement of the equation-of-state parameter hinges on subtle distortions in the Hubble diagram, so we have undertaken a comprehensive set of simulations to understand potential biases introduced by MLCS2k2. Based on simulations of 10,000 light curves that explored the data quality range spanned by the nearby and ESSENCE light curves, we developed a set of quality thresholds

See http://snls.in 2p3.fr /conf /release/.


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TABLE 1 MLCS2k 2 F it Paramete r Quality C uts Fit Parameter
2 ............................................................... Degrees of freedom .................................... à ................................................................ Time of maximum uncertainty .................. First observation with S/ N > 5 ................. Last observation with S/ N > 5 .................

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TABLE 2 SALT Fit Parameter Quality C uts Fit Parameter
2 ............................................................... Degrees of freedom .................................... Stretch ........................................................ Time of maximum uncertainty .................. Observations after B-band maximum ........ First observation with S/ N > 5 .................

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Requirement 2 3 dof ! 4 þ0:4 à 1:7 2:0 rest-frame days +4 days !+9 days cosmoderived grees of four in-

Requirement 2 3 dof ! 5 0:5 s < 1:4 2:0 rest-frame days >1 +5 days

T

maxerr

T

maxerr

Notes.--See Table 4 for the MLCS2k 2 fit parameters used for the logical analysis presented in this paper. These selection criteria were based on Monte Carlo simulations discussed in x 2.5. The number of de freedom is the number of light-curve points with S/ N > 5 minus the dependent MLCS2k 2 fit parameters: mV , à, AV , and Tmax .

Notes.--See Table 5 for the SALT fit parameters used for the cosmological analysis presented in this paper. These selection criteria were based on A06 with additional sanity checks on the stretch parameter and uncertainty in the time of maximum light. The number of degrees of freedom is the number of light-curve points with S/ N > 5 minus the four independent SALT fit parameters: mB , stretch , color, and Tmax .

to require for including SNe Ia in our cosmological analysis. These ``cuts'' are summarized in Table 1. The