Exploring the latest Pantheon SN la dataset by using three kinds of statistics techniques

• 作者: Shuang Wang Xiaolin Luo
• 来源期刊: 理论物理通讯（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: GRAVITATIONAL THEORY, ASTROPHYSICS AND COSMOLOGY
• 页码范围: 92-98
• 页数: 7页
• 语种: 英文

摘要

In this work,we explore the cosmological consequences of the latest Type Ia supernova (SN Ia)dataset,Pantheon,by adopting the wCDM model.The Pantheon dataset currently contains the largest number of SN Ia samples,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 is based on the flux-averaging (FA) method;and (3) improved flux statistics (IFS),which combines the advantages of MS and FS.It should be mentioned that the IFS technique needs to scan the (zcut,Δz) parameters plane,where zcut and Δz are redshift cut-off and redshift interval of FA,respectively.The results are as follows.(1) Using the SN dataset only,the best FA recipe for IFS is (zcut,Δz) =(0.1,0.08);(2) comparing to the old SN dataset,JLA,adopting the Pantheon dataset can reduce the 2σ 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'.