Exploring the Latest Pantheon SNIa Dataset by Using Three Kinds of Statistics Techniques

TL;DR

This paper analyzes the cosmological implications of the extensive Pantheon Type Ia supernova dataset using three statistical techniques, revealing improved constraints on dark energy parameters and comparing their effectiveness.

Contribution

It introduces an improved flux statistics method and evaluates three SN Ia analysis techniques on the largest available dataset for cosmological insights.

Findings

IFS provides tighter constraints on the equation of state w.

Pantheon dataset reduces error bars significantly compared to JLA.

Flux statistics aligns better with other cosmological observations.

Abstract

In this work, we explore the cosmological consequences of the latest Type Ia supernova (SN Ia) data-set, Pantheon, by adopting the $wCDM$ model. The Pantheon data-set is the largest SN Ia samples till now, which contains 1048 supernovae on the redshift range $0 < z < 2.3$. Here we take into account three kinds of SN Ia statistics techniques, including: 1. magnitude statistics (MS), which is the traditional SN Ia statistics technique; 2. flux statistics (FS), which bases on the flux-averaging (FA) method; 3. improved flux statistics (IFS), which combines the advantages of MS and FS. It should be mentioned that, The IFS technique need to scan the $(z_{cut},\Delta z)$ parameters plane, where $z_{cut}$ and $\Delta z$ are redshift cut-off and redshift interval of FA, respectively. The results are shown as follows. (1) Using SN data-set only, the best FA recipe for IFS is $(z_{cut},\Delta z)=(0.1,0.08)$; (2) Comparing to the old SN data-set, JLA, adopting Pantheon data-set can reduce the $2\sigma$ error bars of equation of state $w$ by 38\%, 47\% and 53\% for MS, FS and IFS, respectively; (3) FS gives closer results to other observations, such as Baryon acoustic oscillations and Cosmic microwave background; (4) Compared with FS and IFS, MS more favors a Universe that will end in a "big rip".