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

2020/11/30 by Rubén Arjona, Hai-Nan Lin, Savvas Nesseris +1
Mathematics · Physics and Astronomy · #Astronomy #Astrophysics #COSMIC cancer database #Classical mechanics #Combinatorics #Computer science #Cosmology and Gravitation Theories #Duality (order theory) #Einstein #Einstein Telescope #Galaxy #Gaussian #Gravitation #Gravitational wave #Luminosity #Luminosity distance #Physics #Pulsars and Gravitational Waves Research #Quantum mechanics #Redshift #Relation (database) #Statistical and numerical algorithms #Telescope #Theoretical physics #astro-ph.CO #gr-qc #hep-ph

paper · pdf · doi:10.1103/physrevd.103.103513

published as Phys. Rev. D 103, 103513 (2021) · 15 pages, 6 figures, changes match published version

arxiv created 2021/05/11 · openalex publication_date 2021/05/11 · arxiv updated 2021/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

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.

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