Phantom dark energy as a natural selection of evolutionary processes <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi mathvariant="normal">a</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy="false">^</mml:mo></mml:mrow></mml:mover></mml:mrow></mml:math> <i>la genetic algorithm</i> and cosmological tensions

Abstract

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 $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model.

Topics & Concepts