Compressing SN Ia distance-redshift data to eleven Gaussian log r_p(z) points with covariance is shown to be operationally lossless for cosmological inference across multiple models and datasets.
Extending the supernova Hubble diagram to z~1.5 with the Euclid space mission
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abstract
We forecast dark energy constraints that could be obtained from a new large sample of Type Ia supernovae where those at high redshift are acquired with the Euclid space mission. We simulate a three-prong SN survey: a z<0.35 nearby sample (8000 SNe), a 0.2<z<0.95 intermediate sample (8800 SNe), and a 0.75<z<1.55 high-z sample (1700 SNe). The nearby and intermediate surveys are assumed to be conducted from the ground, while the high-z is a joint ground- and space-based survey. This latter survey, the "Dark Energy Supernova Infra-Red Experiment" (DESIRE), is designed to fit within 6 months of Euclid observing time, with a dedicated observing program. We simulate the SN events as they would be observed in rolling-search mode by the various instruments, and derive the quality of expected cosmological constraints. We account for known systematic uncertainties, in particular calibration uncertainties including their contribution through the training of the supernova model used to fit the supernovae light curves. Using conservative assumptions and a 1-D geometric Planck prior, we find that the ensemble of surveys would yield competitive constraints: a constant equation of state parameter can be constrained to sigma(w)=0.022, and a Dark Energy Task Force figure of merit of 203 is found for a two-parameter equation of state. Our simulations thus indicate that Euclid can bring a significant contribution to a purely geometrical cosmology constraint by extending a high-quality SN Hubble diagram to z~1.5. We also present other science topics enabled by the DESIRE Euclid observations
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Apparent dynamical dark energy signals from SNe Ia with DESI data are consistent with LambdaCDM when accounting for dataset-specific Omega_m inconsistencies rather than requiring evolving dark energy.
citing papers explorer
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Lossless Compression of Cosmological Information from Type Ia Supernova Distance Measurements
Compressing SN Ia distance-redshift data to eleven Gaussian log r_p(z) points with covariance is shown to be operationally lossless for cosmological inference across multiple models and datasets.
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Model-Independent Analysis of Type Ia Supernova Datasets and Implications for Dark Energy
Apparent dynamical dark energy signals from SNe Ia with DESI data are consistent with LambdaCDM when accounting for dataset-specific Omega_m inconsistencies rather than requiring evolving dark energy.