A model where dark energy is created by gravity and interacts with matter can mimic the decreasing running Hubble constant seen in binned supernovae, but only after its free parameter is tuned to that same trend.
Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions
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abstract
Conventional calibration of Baryon Acoustic Oscillations (BAO) data relies on estimation of the sound horizon at drag epoch $r_d$ from early universe observations by assuming a cosmological model. We present a recalibration of two independent BAO datasets, SDSS and DESI, by employing deep learning techniques for model-independent estimation of $r_d$, and explore the impacts on $\Lambda$CDM cosmological parameters. Significant reductions in both Hubble ($H_0$) and clustering ($S_8$) tensions are observed for both the recalibrated datasets. Moderate shifts in some other parameters hint towards further exploration of such data-driven approaches.
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Two Dynamical Scenarios for Binned Master Sample Interpretation
A model where dark energy is created by gravity and interacts with matter can mimic the decreasing running Hubble constant seen in binned supernovae, but only after its free parameter is tuned to that same trend.