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◆ Journal of Cosmology and Astroparticle Physics2025-12-01· Physics

Revisiting the phenomenologically emergent dark energy model: is non-zero equation of state of dark matter favored by DESI DR2?

Tian-Nuo Li, Yi-Min Zhang, Yan-Hong Yao, Guo-Hong Du, Peng-Ju Wu, Jing-Fei Zhang, Xin Zhang

原始摘要(英文原文)· Original abstract
Abstract The nature of dark matter remains one of the most fundamental and unresolved questions in modern cosmology. In most cosmological models, dark matter is typically modeled as pressureless dust with an equation of state (EoS) parameter w dm = 0. However, there is no fundamental theoretical reason to exclude the possibility of a non-zero dark matter EoS parameter. In this work, we explore the possibility of a non-zero dark matter EoS within the phenomenologically emergent dark energy (PEDE) model, given its simplicity and proven ability to alleviate the Hubble tension. We perform observational constraints by using the latest baryon acoustic oscillation data from DESI DR2, the cosmic microwave background (CMB) data from Planck, and the type Ia supernova data from DESY5 and PantheonPlus. From our analysis, we observe that a negative dark matter EoS parameter is preferred in all scenarios. Specifically, the CMB+DESI+DESY5 data yields w dm = -0.00093 ± 0.00032, deviating from zero at approximately the 3σ level. However, this deviation is likely driven by unidentified systematics or inconsistencies in the DESY5 data, with the deviation decreasing to 2σ when using PantheonPlus data. Meanwhile, a negative w dm would increase the Hubble tension due to the positive degeneracy between w dm and H 0 . Furthermore, Bayesian evidence suggests that the ΛCDM model is strongly preferred over the PEDE+w_ dm model. These analyses illustrate that it is not possible to both support a non-zero dark matter component within the PEDE model and alleviate the Hubble tension simultaneously.
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Revisiting the phenomenologically emergent dark energy model: is non-zero equation of state of dark matter favored by DESI DR2? — 科研速览 Science Skim