Utilizing Type Ia Supernovae in a Large, Fast, Imaging Survey to Constrain Dark Energy
Andrew R. Zentner, Suman Bhattacharya (University of Pittsburgh)

TL;DR
This paper explores how large, rapid imaging surveys of Type Ia supernovae can effectively constrain dark energy properties by combining luminosity distance data and flux dispersion, even with photometric redshift uncertainties.
Contribution
It demonstrates that supernova flux dispersion and mean distance measurements together can break degeneracies in dark energy parameters without high-redshift data, emphasizing the importance of spectroscopic calibration.
Findings
Imaging surveys can constrain dark energy with 1-sigma error of 0.03-0.09.
Spectroscopic calibration with ~1000 supernovae suffices for optimal constraints.
Calibration with high-redshift supernovae improves redshift accuracy and dark energy constraints.
Abstract
We study the utility of a large sample of type Ia supernovae that might be observed in an imaging survey that rapidly scans a large fraction of the sky for constraining dark energy. We consider information from the traditional luminosity distance test as well as the spread in SNeIa fluxes at fixed redshift induced by gravitational lensing. We include a treatment of photometric redshift uncertainties in our analysis. Our primary result is that the information contained in the mean distance moduli of SNeIa and the dispersion among SNeIa distance moduli complement each other, breaking a degeneracy between the present dark energy equation of state and its time variation without the need for a high-redshift supernova sample. To address photometric redshift uncertainties, we present dark energy constraints as a function of the size of an external set of spectroscopically-observed SNeIa that…
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