Solar energy paper index
Cosmological consequences of a dynamical dark matter in the light of DESI DR2 measurements
One-line summary
A solar energy research paper on Cosmological consequences of a dynamical dark matter in the light of DESI DR2 measurements.
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Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
Recent DESI results exhibit preference for a Null Energy Condition violating dynamical dark energy, with early phantom behaviour. We explore an alternative interpretation in which this preference arises from unconventional dark matter dynamics rather than from dynamical dark energy. We propose a dynamical dark matter (DDM) model, with a non-zero equation of state (EoS) that smoothly interpolates between early time and late time asymptotes across a transition scale factor $a_t$, and study its consequences against cosmological datasets including CMB, DESI DR2 BAO, SNeIa (PantheonPlus, Union3, and DESY5) and growth rate data. We find the early time EoS to be consistent with zero, while the present day value is negative at a significance ranging from $0.42σ$ to $3.02σ$ depending on the dataset combination. The strongest preference occurs from the combination of CMB, DESI, and DESY5 giving the present day EoS to be $-0.060^{+0.013}_{-0.028}$ and $a_t = 0.41^{+0.088}_{-0.13}$ at 68\% CL. This preference for a non-zero, late time DM EoS persists when growth rate data are included and across all three SNeIa compilations considered, while the matter density $Ω_m$ mildly shifts to higher values relative to $Λ$CDM. The model also predicts a lower $σ_8$ and $S_8$ than $Λ$CDM, in better agreement with weak-lensing data, while $H_0$ remains unchanged and in tension with local distance-ladder measurements. The DDM model is preferred over $Λ$CDM ($Δχ^2_{\rm MAP} = -14.093$, $Δ{\rm DIC} = -7.838$ for Planck+DESI+DESY5) but disfavored relative to the CPL parameterization of DE ($Δχ^2_{\rm MAP} = 6.755$, $Δ{\rm DIC} = 7.966$). This preference is consistent among other combination of datasets as well.
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