Marginal evidence for cosmic acceleration from Type Ia supernovae (1506.01354v3)

Abstract: The "standard" model of cosmology is founded on the basis that the expansion rate of the universe is accelerating at present --- as was inferred originally from the Hubble diagram of Type Ia supernovae. There exists now a much bigger database of supernovae so we can perform rigorous statistical tests to check whether these "standardisable candles" indeed indicate cosmic acceleration. Taking account of the empirical procedure by which corrections are made to their absolute magnitudes to allow for the varying shape of the light curve and extinction by dust, we find, rather surprisingly, that the data are still quite consistent with a constant rate of expansion.

• The paper re-evaluates Type Ia supernovae data, finding only marginal statistical evidence (≲3σ) for cosmic acceleration, challenging previous strong claims.
• The authors employ an improved maximum likelihood estimator (MLE) that better accounts for intrinsic and experimental uncertainties in supernovae data analysis.
• Their analysis suggests a constant expansion model (Milne model) fits the data comparably well to the accelerating universe model (ΛCDM), prompting a re-examination of dark energy evidence.

Overview of "Marginal evidence for cosmic acceleration from Type Ia supernovae"

The paper, "Marginal evidence for cosmic acceleration from Type Ia supernovae," presents a rigorous statistical analysis of data from Type Ia supernovae (SN Ia) to assess the current understanding of cosmic acceleration. The paper challenges the prevailing paradigm within the standard cosmological model, which postulates that the universe’s expansion is accelerating due to dark energy. Authored by Nielsen, Guffanti, and Sarkar, the paper re-evaluates a large dataset of SN Ia, considering possible biases and errors in established methodologies.

The analysis employs an improved maximum likelihood estimator (MLE) approach on the Joint Lightcurve Analysis (JLA) catalogue, encompassing 740 SN Ia, processed via the SALT2 method. This approach is compared to previously used techniques like $\chi^2$ minimization, which have faced criticism for their treatment of cosmic parameters and nuisance parameters within their analyses.

Key Findings

1. Cosmic Expansion Consistency:
   • The authors report that upon re-evaluation, the extensive SN Ia dataset offers marginal evidence for cosmic acceleration, at a level of $\lesssim 3\sigma$. This contrasts with earlier assertions based on simpler statistical methods that strongly supported an accelerating expansion model.
2. Likelihood Estimator Methodology:
   • The paper introduces a likelihood function model crafted to include variances from intrinsic SN Ia distributions and experimental uncertainties. This model allows for more robust parameter fitting and claims to provide more reliable goodness-of-fit statistics, vital for comparing different cosmological models.
3. Gaussian Models for Standardization Parameters:
   • The paper adopts Gaussian distributions to model the uncertainties in key SN Ia parameters, such as the apparent magnitude at maximum ($m_B^*$), and light curve shape ($x_1$) and color ($c$) corrections. The resulting likelihood function explicitly accounts for these, unlike previous analyses adopting flat distributions that could distort parameter estimates.
4. Comparison with $\Lambda$CDM:
   • The paper compares the best-fit accelerating universe ($\Lambda$CDM) against a model wherein the universe expands at a constant rate (Milne model). The findings suggest the Milne model aligns well with the data, challenging the necessity of a cosmological constant as currently postulated.
5. Assessment of Model Fit:
   • A pull distribution confirms the adequacy of the Gaussian model for standardization parameters, and a Monte Carlo simulation validates the method’s self-consistency. Compared to older methods, the new likelihood approach provides a statistically transparent means of model comparison.

Implications and Future Work

The paper’s findings have significant implications for cosmology, especially concerning the empirical evidence for dark energy as the driver of cosmic acceleration. By casting doubt on previous conclusions, the paper prompts a re-examination of the foundations of the standard cosmological model. The researchers argue for the continued development and refinement of statistical methods essential for decoding the vast datasets available today.

Future work should build upon these findings by exploring alternative models that could account for the observed cosmic expansion without invoking dark energy or a cosmological constant. Observational strategies, such as those planned for the CODEX experiment on the European Extremely Large Telescope, could independently verify these claims through direct measurements of the redshift drift over time. These advancements will be crucial for developing a more consistent cosmological framework that aligns closely with empirical observations.