Dark matter component decaying after recombination: lensing constraints with Planck data

TL;DR

The paper tests a two‑component dark matter scenario in which a subdominant fraction $F$ decays after recombination with width $\Gamma$, aiming to reconcile high‑z and low‑z cosmological tensions. Using Planck TT,TE,EE + lowP data, direct lensing $C_l^{\phi\phi}$, and conflicting low‑redshift measurements ($H_0$ and cluster counts), the authors implement a numerical analysis with CLASS and Monte Python to map the $F$–$\Gamma$ parameter space. They find that Planck lensing data strongly constrain $F$ to be small ($F<0.07$ from TT+lowP; $F<0.04$ when lowP is included; $F<0.08$ from $C_l^{\phi\phi}$), while combining Planck with low‑z data can favor $F\approx2$–$5\%$ but only yield modest improvements over $\Lambda$CDM due to lensing tensions. The results demonstrate that Planck lensing provides a powerful, dual constraint on decaying DM scenarios, and resolving the observed lensing tension is crucial for a definitive assessment of such models’ viability.

Abstract

It has been recently suggested~\cite{Berezhiani:2015yta} that emerging tension between cosmological parameter values derived in high-redshift (CMB anisotropy) and low-redshift (cluster counts, Hubble constant) measurements can be reconciled in a model which contains subdominant fraction of dark matter decaying after recombination. We check the model against the CMB Planck data. We find that lensing of the CMB anisotropies by the large-scale structure gives strong extra constraints on this model, limiting the fraction as $F<8\%$ at 2\,$σ$ confidence level. However, investigating the combined data set of CMB and conflicting low-$z$ measurements, we obtain that the model with $F\approx2\!-\!5$\% exhibits better fit (by 1.5-3\,$σ$ depending on the lensing priors) compared to that of the concordance $Λ$CDM cosmological model.

Figures (5)

• Figure 1: Data points with error bars show residuals after subtraction from the measured TT power spectrum the $\Lambda$CDM model prediction with the best-fit parameters from $\rm TT,TE,EE + lowP$ analysis. Solid curve corresponds to the difference between TT spectra in DDM ($F=0.1$, $\Gamma=2000$ km/s/Mpc) and the same $\Lambda$CDM model.
• Figure 2: Data points with error bars show residuals after subtraction from the directly measured lensing power spectrum the $\Lambda$CDM model prediction with the best-fit parameters from $\rm TT,TE,EE + lowP$ analysis. Solid and dotted curves show the difference between $C_{l}^{\phi\phi}$ in DDM and the same $\Lambda$CDM model: for the solid curve $F=0.1$, $\Gamma=2000$ km/s/Mpc were used as the DDM parameters, while the dotted curve corresponds to the best-fit DDM in the $\rm TT,TE,EE + lowP + 4lens$ analysis.
• Figure 3: Posterior distributions ($1\,\sigma$ and $2\,\sigma$ contours) of parameters $F$, $\Gamma$ in DDM model. Tags are described in Table \ref{['tab:sets']}.
• Figure 4: Same as Fig. \ref{['fig:7']} but for $H_{0}$ and $F$.
• Figure 5: Same as Fig. \ref{['fig:7']} but for $\sigma_8$ and $\Omega_m$.