Non-parametric spatial curvature inference using late-universe cosmological probes
Suhail Dhawan, Justin Alsing, Sunny Vagnozzi

TL;DR
This paper introduces a non-parametric method using Gaussian Processes to infer the spatial curvature of the universe from late-Universe cosmological probes, avoiding assumptions about the expansion history.
Contribution
The authors develop a model-independent approach combining Cosmic Chronometers and Type Ia Supernovae data to constrain spatial curvature, insensitive to calibration and early-Universe assumptions.
Findings
Current data yields .03 b1 0.26 for .
Method is robust against calibration uncertainties.
Forecasts suggest future surveys can constrain .0 at the 1% level.
Abstract
Inferring high-fidelity constraints on the spatial curvature parameter, , under as few assumptions as possible, is of fundamental importance in cosmology. We propose a method to non-parametrically infer from late-Universe probes alone. Using Gaussian Processes (GP) to reconstruct the expansion history, we combine Cosmic Chronometers (CC) and Type Ia Supernovae (SNe~Ia) data to infer constraints on curvature, marginalized over the expansion history, calibration of the CC and SNe~Ia data, and the GP hyper-parameters. The obtained constraints on are free from parametric model assumptions for the expansion history, and are insensitive to the overall calibration of both the CC and SNe~Ia data (being sensitive only to relative distances and expansion rates). Applying this method to \textit{Pantheon} SNe~Ia and the latest compilation of CCs, we…
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