arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.02718astro-ph.COgr-qchep-ph
收藏

Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events

Rubén Arjona, Hai-Nan Lin, Savvas Nesseris, Li Tang

英文摘要:

We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model. In particular, we use two machine learning approaches, the Genetic Algorithms and Gaussian Processes, to reconstruct the mock data and we show that both approaches are capable of correctly recovering the underlying fiducial model and can provide percent-level constraints at intermediate redshifts when applied to future Einstein Telescope data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑