A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning
Rahul Shah, Arko Bhaumik, Purba Mukherjee, Supratik Pal

TL;DR
This paper evaluates eLISA's potential to resolve the Hubble tension by combining Fisher forecasts, MCMC, and machine learning, showing eLISA could significantly improve constraints on the Hubble constant.
Contribution
It introduces a comprehensive analysis using Fisher, MCMC, and Gaussian Processes to forecast eLISA's ability to address the Hubble tension across various cosmological models.
Findings
eLISA can constrain H0 at sub-percent precision.
MCMC and GP methods reduce tensions in certain models.
No significant change for models with lesser current tension.
Abstract
We carry out an in-depth analysis of the capability of the upcoming space-based gravitational wave mission eLISA in addressing the Hubble tension, with a primary focus on observations at intermediate redshifts (). We consider six different parametrizations representing different classes of cosmological models, which we constrain using the latest datasets of cosmic microwave background (CMB), baryon acoustic oscillations (BAO), and type Ia supernovae (SNIa) observations, in order to find out the up-to-date tensions with direct measurement data. Subsequently, these constraints are used as fiducials to construct mock catalogs for eLISA. We then employ Fisher analysis to forecast the future performance of each model in the context of eLISA. We further implement traditional Markov Chain Monte Carlo (MCMC) to estimate the parameters from the simulated catalogs. Finally, we utilize…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Geophysics and Gravity Measurements · Statistical and numerical algorithms
