Reconciling early and late time tensions with Reinforcement Learning
Sharma M.K. Sami M.
1 May 2025Institute of Physics
Journal of Cosmology and Astroparticle Physics
2025#2025Issue 5
We study the possibility of accommodating both early and late-time tensions using a novel reinforcement learning technique. By applying this technique, we aim to optimize the evolution of the Hubble parameter from recombination to the present epoch, addressing both tensions simultaneously. To maximize the goodness of fit, our learning technique achieves a fit that surpasses even the ΛCDM model. Our results demonstrate a tendency to weaken both early and late time tensions in a completely model-independent manner.
dark energy theory , Machine learning , modified gravity
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Centre For Cosmology and Science Popularization, SGT University, Haryana, 122505, India
Eurasian International Centre for Theoretical Physics, Astana, Kazakhstan
Chinese Academy of Sciences, 52 Sanlihe Rd, Xicheng District, Beijing, China
Centre For Cosmology and Science Popularization
Eurasian International Centre for Theoretical Physics
Chinese Academy of Sciences
10 лет помогаем публиковать статьи Международный издатель
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