A Sparse Spectroscopic Supernova Survey

TL;DR

This paper proposes a new, efficient supernova survey method that uses sparse spectroscopic data at maximum light to accurately estimate distances and constrain dark energy, reducing traditional time requirements.

Contribution

It introduces an optimized, condensed spectroscopic survey approach that focuses on specific redshifts to improve efficiency and accuracy in supernova cosmology.

Findings

Optimized redshift selection enhances survey efficiency.

Sparse spectra at maximum light can yield accurate distance estimates.

The method constrains dark energy parameters effectively.

Abstract

Supernova cosmology surveys are traditionally time consuming, especially for the critical spectroscopic data. However, a single spectrum at maximum light may provide accurate distance estimation if recent developments hold. This could open up a new type of supernova cosmology survey, with a useful interaction between the spectra and a focus on specific redshifts. We optimize the redshift selection and show that this condensed survey could efficiently deliver highly accurate dark energy constraints.