The paper "Dynamical dark energy in the light of DESI 2024 data" by Nandan Roy explores the implications of recent findings from the Dark Energy Spectroscopic Instrument (DESI) data release 1 (DR1) on the nature of dark energy. The study focuses on the Chevallier-Polarski-Linder (CPL) parameterization of the dark energy equation of state (EoS) and suggests a possible phantom barrier crossing in the recent past. However, the paper highlights issues with the CPL parameterization, such as prior selection and parameter degeneracies. To address these issues, the author proposes an alternative two-parameter parameterization of the dark energy EoS, which behaves like the cosmological constant at higher redshifts and deviates from the CPL form at lower redshifts. This new model is shown to align with the DESI results, indicating a transition from phantom to quintessence dark energy. The model significantly reduces the Hubble tension, reducing it to about 2.8σ when compared to Hubble Space Telescope and SH0ES data, and to 1.6σ with standardized TRGB and Type Ia supernova data. Bayesian model selection using Bayes factors and Akaike Information Criteria (AIC) strongly prefers the proposed model over the $\Lambda$CDM model, supporting the dynamical nature of dark energy. The paper also evaluates the model's performance at the linear perturbation level, showing consistency with the $\Lambda$CDM model at high multipoles and large scales. Overall, the study provides a robust framework for understanding the dynamics of dark energy and its implications for cosmology.The paper "Dynamical dark energy in the light of DESI 2024 data" by Nandan Roy explores the implications of recent findings from the Dark Energy Spectroscopic Instrument (DESI) data release 1 (DR1) on the nature of dark energy. The study focuses on the Chevallier-Polarski-Linder (CPL) parameterization of the dark energy equation of state (EoS) and suggests a possible phantom barrier crossing in the recent past. However, the paper highlights issues with the CPL parameterization, such as prior selection and parameter degeneracies. To address these issues, the author proposes an alternative two-parameter parameterization of the dark energy EoS, which behaves like the cosmological constant at higher redshifts and deviates from the CPL form at lower redshifts. This new model is shown to align with the DESI results, indicating a transition from phantom to quintessence dark energy. The model significantly reduces the Hubble tension, reducing it to about 2.8σ when compared to Hubble Space Telescope and SH0ES data, and to 1.6σ with standardized TRGB and Type Ia supernova data. Bayesian model selection using Bayes factors and Akaike Information Criteria (AIC) strongly prefers the proposed model over the $\Lambda$CDM model, supporting the dynamical nature of dark energy. The paper also evaluates the model's performance at the linear perturbation level, showing consistency with the $\Lambda$CDM model at high multipoles and large scales. Overall, the study provides a robust framework for understanding the dynamics of dark energy and its implications for cosmology.