Improved Cosmological Constraints from New, Old and Combined Supernova Datasets
M.Kowalski, D.Rubin, G.Aldering, R.J.Agostinho, A.Amadon, R.Amanullah,, C.Balland, K. Barbary, G.Blanc, P.J.Challis, A.Conley, N.V.Connolly,, R.Covarrubias, K.S.Dawson, S.E.Deustua, R.Ellis, S.Fabbro, V.Fadeyev, X.Fan,, B.Farris, G.Folatelli, B.L.Frye, G.Garavini, E.L.Gates

TL;DR
This paper compiles and analyzes a large, heterogeneous set of Type Ia supernova data using a consistent method to improve cosmological constraints on dark energy and the universe's composition.
Contribution
It introduces a new, unified dataset of 414 supernovae with a consistent analysis procedure, enhancing the precision of cosmological parameter estimation.
Findings
Supernova data constrains dark energy density to $oxed{ ext{Ω}_ ext{Λ}=0.713^{+0.027}_{-0.029}}$
Combined supernova, CMB, and BAO data suggest $w eq -1$ but with weak constraints at high redshift.
Current supernova data provide the tightest constraints on cosmological parameters to date.
Abstract
We present a new compilation of Type Ia supernovae (SNe Ia), a new dataset of low-redshift nearby-Hubble-flow SNe and new analysis procedures to work with these heterogeneous compilations. This ``Union'' compilation of 414 SN Ia, which reduces to 307 SNe after selection cuts, includes the recent large samples of SNe Ia from the Supernova Legacy Survey and ESSENCE Survey, the older datasets, as well as the recently extended dataset of distant supernovae observed with HST. A single, consistent and blind analysis procedure is used for all the various SN Ia subsamples, and a new procedure is implemented that consistently weights the heterogeneous data sets and rejects outliers. We present the latest results from this Union compilation and discuss the cosmological constraints from this new compilation and its combination with other cosmological measurements (CMB and BAO). The constraint we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
