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Improved Cosmological Constraints from New, Old and Combined Supernova Datasets

M. Kowalski, D. Rubin, G. Aldering, R. J. Agostinho, A. Amadon, R. Amanullah, C. Balland, K. Barbary, G. Blanc, P. J. Challis, A. Conley, N. V. Connolly, R. Covarrubias, K. S. Dawson, S. E. Deustua, R. Ellis, S. Fabbro, V. Fadeyev, X. Fan, B. Farris, G. Folatelli, B. L. Frye, G. Garavini, E. L. Gates, L. Germany, G. Goldhaber, B. Goldman, A. Goobar, D. E. Groom, J. Haissinski, D. Hardin, I. Hook, S. Kent, A. G. Kim, R. A. Knop, C. Lidman, E. V. Linder, J. Mendez, J. Meyers, G. J. Miller, M. Moniez, A. M. Mourao, H. Newberg, S. Nobili, P. E. Nugent, R. Pain, O. Perdereau, S. Perlmutter, M. M. Phillips, V. Prasad, R. Quimby, N. Regnault, J. Rich, E. P. Rubenstein, P. Ruiz-Lapuente, F. D. Santos, B. E. Schaefer, R. A. Schommer, R. C. Smith, A. M. Soderberg, A. L. Spadafora, L. -G. Strolger, M. Strovink, N. B. Suntzeff, N. Suzuki, R. C. Thomas, N. A. Walton, L. Wang, W. M. Wood-Vasey, J. L. Yun

TL;DR

The paper presents the Union compilation, a large, heterogeneous set of 414 Type Ia SNe (307 after cuts), unified under a consistent, blind analysis to derive cosmological constraints. It introduces rigorous data reduction, band-pass calibration, SALT-based lightcurve fitting, and robust outlier handling, with explicit treatment of systematics and sample-heterogeneity. Joint analyses with CMB and BAO yield tight constraints consistent with a flat LCDM Universe (e.g., w ≈ -0.97) and demonstrate that the SN data alone offer limited sensitivity to w(z) evolution, though the framework allows testing such evolution as data improve. The study also confirms the value of nearby Hubble-flow SNe in tightening constraints and provides a publicly accessible dataset and methodology for future work.

Abstract

We present a new compilation of Type Ia supernovae (SNe Ia), a new dataset of low-redshift nearby-Hubble-flow SNe and new analysis procedures to work with these heterogeneous compilations. This ``Union'' compilation of 414 SN Ia, which reduces to 307 SNe after selection cuts, includes the recent large samples of SNe Ia from the Supernova Legacy Survey and ESSENCE Survey, the older datasets, as well as the recently extended dataset of distant supernovae observed with HST. A single, consistent and blind analysis procedure is used for all the various SN Ia subsamples, and a new procedure is implemented that consistently weights the heterogeneous data sets and rejects outliers. We present the latest results from this Union compilation and discuss the cosmological constraints from this new compilation and its combination with other cosmological measurements (CMB and BAO). The constraint we obtain from supernovae on the dark energy density is $Ω_Λ= 0.713^{+0.027}_{-0.029} (stat)}^{+0.036}_{-0.039} (sys)}$, for a flat, LCDM Universe. Assuming a constant equation of state parameter, $w$, the combined constraints from SNe, BAO and CMB give $w=-0.969^{+0.059}_{-0.063}(stat)^{+0.063}_{-0.066} (sys)$. While our results are consistent with a cosmological constant, we obtain only relatively weak constraints on a $w$ that varies with redshift. In particular, the current SN data do not yet significantly constrain $w$ at $z>1$. With the addition of our new nearby Hubble-flow SNe Ia, these resulting cosmological constraints are currently the tightest available.

Improved Cosmological Constraints from New, Old and Combined Supernova Datasets

TL;DR

The paper presents the Union compilation, a large, heterogeneous set of 414 Type Ia SNe (307 after cuts), unified under a consistent, blind analysis to derive cosmological constraints. It introduces rigorous data reduction, band-pass calibration, SALT-based lightcurve fitting, and robust outlier handling, with explicit treatment of systematics and sample-heterogeneity. Joint analyses with CMB and BAO yield tight constraints consistent with a flat LCDM Universe (e.g., w ≈ -0.97) and demonstrate that the SN data alone offer limited sensitivity to w(z) evolution, though the framework allows testing such evolution as data improve. The study also confirms the value of nearby Hubble-flow SNe in tightening constraints and provides a publicly accessible dataset and methodology for future work.

Abstract

We present a new compilation of Type Ia supernovae (SNe Ia), a new dataset of low-redshift nearby-Hubble-flow SNe and new analysis procedures to work with these heterogeneous compilations. This ``Union'' compilation of 414 SN Ia, which reduces to 307 SNe after selection cuts, includes the recent large samples of SNe Ia from the Supernova Legacy Survey and ESSENCE Survey, the older datasets, as well as the recently extended dataset of distant supernovae observed with HST. A single, consistent and blind analysis procedure is used for all the various SN Ia subsamples, and a new procedure is implemented that consistently weights the heterogeneous data sets and rejects outliers. We present the latest results from this Union compilation and discuss the cosmological constraints from this new compilation and its combination with other cosmological measurements (CMB and BAO). The constraint we obtain from supernovae on the dark energy density is , for a flat, LCDM Universe. Assuming a constant equation of state parameter, , the combined constraints from SNe, BAO and CMB give . While our results are consistent with a cosmological constant, we obtain only relatively weak constraints on a that varies with redshift. In particular, the current SN data do not yet significantly constrain at . With the addition of our new nearby Hubble-flow SNe Ia, these resulting cosmological constraints are currently the tightest available.

Paper Structure

This paper contains 28 sections, 10 equations, 24 figures, 7 tables.

Figures (24)

  • Figure 1: Band passes for the various instruments used in the Spring 1999 Nearby Supernova Campaign. For comparison, the filled regions represent the pass band transmission functions of the bessel system.
  • Figure 2: SNe lightcurves of the SCP Nearby 1999 campaign. The filled symbols represent the S-corrected data, the empty symbols the raw photometric data. Both the S-corrected data as well as the model parameterization (dashed line) are shown to guide the eye only and are not used any further in the remaining paper.
  • Figure 3: B and V lightcurves and residuals. The multiple curves represent the model predictions for the different band passes, and are obtained by integrating the product of passband and the redshifted spectral-template.
  • Figure 3: Continued
  • Figure 3: Continued
  • ...and 19 more figures