Phantom dark energy as a natural selection of evolutionary processes a ^ la genetic algorithm and cosmological tensions

Gangopadhyay M.R. Sami M. Sharma M.K.
15 November 2023 American Physical Society

Physical Review D
2023 #108 Issue 10

We study the late-time cosmological tensions using the low-redshift background and redshift-space distortion data by employing a machine learning (ML) technique. By comparing the generated observables with the standard cosmological scenario, our findings indicate support for the phantom nature of dark energy, which ultimately leads to a reduction in the existing tensions. The model-independent approach also enables us to examine the combined background and perturbative history, where tensions are reduced. Moreover, from a statistical perspective, we have shown that our results exhibit a better fit to the data when compared to the ΛCDM model.

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Centre for Cosmology and Science Popularization (CCSP), SGT University, Haryana, Gurugram, 122505, India
Center for Theoretical Physics, Eurasian National University, Astana, 010008, Kazakhstan
Chinese Academy of Sciences, 52 Sanlihe Rd, Xicheng District, Beijing, China

Centre for Cosmology and Science Popularization (CCSP)
Center for Theoretical Physics
Chinese Academy of Sciences

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