Observational Constraints on the Nature of the Dark Energy: First Cosmological Results from the ESSENCE Supernova Survey

TL;DR

This study uses 60 ESSENCE Type Ia supernovae, supplemented by nearby and SNLS data, to constrain the dark energy equation-of-state parameter $w$ in a flat Universe, employing MLCS2k2 (glosz) as the primary distance fitter and cross-checking with SALT. It conducts extensive simulations to quantify and mitigate systematics, with host-galaxy extinction priors identified as a major current limitation. By combining ESSENCE with BAO measurements, the authors obtain $w$ values near -1 (e.g., $w=-1.069^{+0.091}_{-0.093}$ with $\Omega_M$ around 0.27, and similar results when including SNLS and Riess gold samples), indicating consistency with a cosmological constant. The results underscore the importance of extinction modeling and calibration in SN cosmology and chart a path toward tighter constraints through larger samples and improved photometric calibrations in upcoming analyses. Overall, the ESSENCE results, especially in combination with SNLS and BAO data, reinforce the $\Lambda$CDM paradigm while identifying the dominant systematic avenues to reduce for future precision measurements of $w$ and its possible evolution.

Abstract

We present constraints on the dark energy equation-of-state parameter, w=P/(rho c^2), using 60 Type Ia supernovae (SNe 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 (Omega_M, w) from baryon acoustic oscillations, we obtain a value for a static equation-of-state parameter w=-1.05^{+0.13}_{-0.12} (stat; 1 sigma) +- 0.11 (sys) and Omega_M=0.274^{+0.033}_{-0.020} (stat; 1 sigma) with a best-fit chi^2/DoF of 0.96. These results are consistent with those reported by the SuperNova Legacy Survey in 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 currently 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 SuperNova Legacy Survey SNe Ia, we obtain a joint constraint of w=-1.07^{+0.09}_{-0.09} (stat; 1 sigma) +- 0.12 (sys), Omega_M=0.267^{+0.028}_{-0.018} (stat; 1 sigma) with a best-fit chi^2/DoF of 0.91. The current SN Ia data are fully consistent with a cosmological constant.

Figures (13)

• Figure 1: The distribution of the MLCS2k2 light-curve width parameter $\Delta$ and $A_V$ for the MLCS2k2 fits with the "glosz" prior to the nearby (dotted line), ESSENCE (solid line), and SNLS (dashed line) SNe Ia considered in this paper. The "glosz" prior (dotted-dashed line) is shown here for $z=0$ where it is equivalent to the "glos" prior. Note that we are mixing two slightly different things in showing the prior with these estimated mean fit parameters. The prior, which directly relates to the mode, is not expected to match the a posteriori mean distribution of the fit parameters. See Fig. \ref{['fig:cut']} for the ESSENCE selection effect as a function of redshift. See Table \ref{['tab:mlcs_fits_prior_glosz']} for the full set of MLCS2k2 light-curve fit results for these SNe Ia.
• Figure 2: The distribution of the SALT light-curve stretch and the estimated color plus extinction for the nearby (dotted line), ESSENCE (solid line), and SNLS (dashed line) SNe Ia considered in this paper. The priors for SALT are effectively flat for stretch and color, and SALT quotes minimum $\chi^2$ values instead of the estimated mean parameter values of MLCS2k2. See Table \ref{['tab:salt_fits']} for the full set of SALT light-curve fit results for these SNe Ia.
• Figure 3: The median of the distance modulus error as a function of redshift for the simulated data sets. The points show the median value of the difference between the input $\mu_{true}$ and recovered $\mu_{obs}$ of about 1000 simulated supernovae at each redshift. The lines indicate the root-mean-square spread of the recovered distance modulus.
• Figure 4: The recovered distribution of visual extinctions for simulated supernovae in the ESSENCE sample if the input distribution were uniform in $A_V$ out to large extinctions. The curves are fit to determine the parameters of the window function (see Table \ref{['tab:gloszwindow']}) which is then used to modify the "glos" prior a function of redshift into the "glosz" prior. We estimate the SNLS selection function as extending $+0.2$ in redshift deeper than the ESSENCE selection function.
• Figure 5: Distance modulus, $\mu$, residuals with respect to a $\Lambda{\rm CDM}$ cosmology as a function of the MLCS2k2 "glosz" fit parameters: $\Delta$ and $A_V$. See Table \ref{['tab:mlcs_fits_prior_glosz']}.