Characterising Dark Energy through supernovae

TL;DR

This paper reviews how Type Ia supernovae observations can be used to investigate the nature of dark energy and modifications to gravity, discussing various models, data analysis methods, and additional cosmological information.

Contribution

It provides a comprehensive overview of methods and models for characterizing dark energy using supernovae, including non-standard approaches and statistical inference techniques.

Findings

Supernovae can distinguish between dark energy and modified gravity models.

Supernova data can inform gravitational lensing and peculiar velocities.

Different statistical methods are crucial for model comparison.

Abstract

Type Ia supernovae are a powerful cosmological probe, that gave the first strong evidence that the expansion of the universe is accelerating. Here we provide an overview of how supernovae can go further to reveal information about what is causing the acceleration, be it dark energy or some modification to our laws of gravity. We first summarise the many different approaches used to explain or test the acceleration, including parametric models (like the standard model, LambdaCDM), non-parametric models, dark fluid models such as quintessence, and extensions to standard gravity. We also show how supernova data can be used beyond the Hubble diagram, to give information on gravitational lensing and peculiar velocities that can be used to distinguish between models that predict the same expansion history. Finally, we review the methods of statistical inference that are commonly used, making a point of separating parameter estimation from model selection.