Evidence for Evolving Dark Energy from LRG1 and Low-$z$ SNe Ia Data

Abstract: We present observational evidences indicating that dark energy may be evolving with time, thereby challenging the core assumptions of the standard $\Lambda$CDM model. Our investigation extends beyond the standard $\Lambda$CDM model by exploring a range of dynamical dark energy models. The analysis reveals that the matter density parameter, $\Omega_m$, changes with redshift, and this change is significantly influenced by the LRG1 datapoint from the DESI DR2 survey. We find that including LRG1 strongly affects the predictions of these models, especially for $\Omega_m$ and $\omega_0$, with the latter shifting from $-1$ to slightly higher values. This suggests that the evidence of evolving dark energy in the DESI DR2 data is driven by the LRG1 datapoint. When we exclude low-redshift supernovae data, particularly from the DES-SN5YR compilation, the model predictions are restored to the $\Lambda$CDM paradigm. This effect is particularly evident in the GEDE model, where removing low-redshift supernovae data leads to $\Delta = 0$, effectively recovering $\Lambda$CDM paradigm. Our analysis confirms that the evidence for dynamical dark energy is substantial, particularly at low redshift ($z \lesssim 0.3$). The reconstruction of $\omega(z)$ and $f_{DE}(z)$, using a combination of DESI DR2 BAO measurements, CMB distance priors, and supernovae datasets, further supports the evolving nature dark energy. These results favor a dynamical dark-energy scenario characterized by $\omega_0>-1$, $\omega_a<0$, and $\omega_0+\omega_a<-1$ (Quintom-B). Bayesian analysis of model evidence reveals that the inclusion of low-redshift supernovae data significantly strengthens the support for evolving dark energy models such as JBP and Mirage, while models revert to inconclusive or weak support when low-redshift data are excluded.