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

Rubén Arjona, Hai-Nan Lin, Savvas Nesseris, and Li Tang
Phys. Rev. D 103, 103513 – Published 11 May 2021

Abstract

We use simulated strongly lensed gravitational wave events from the Einstein telescope to demonstrate how the luminosity and angular diameter distances, dL(z) and dA(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.

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  • Received 9 November 2020
  • Accepted 14 April 2021

DOI:https://doi.org/10.1103/PhysRevD.103.103513

© 2021 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Rubén Arjona1,*, Hai-Nan Lin2,†, Savvas Nesseris1,‡, and Li Tang3,2,§

  • 1Instituto de Física Teórica UAM-CSIC, Universidad Autonóma de Madrid, Cantoblanco, 28049 Madrid, Spain
  • 2Department of Physics, Chongqing University, Chongqing 401331, China
  • 3Department of Math and Physics, Mianyang Normal University, Mianyang 621000, China

  • *ruben.arjona@uam.es
  • linhn@cqu.edu.cn
  • savvas.nesseris@csic.es
  • §tang@cqu.edu.cn

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Vol. 103, Iss. 10 — 15 May 2021

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