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Relieving the Tension between Weak Lensing and Cosmic Microwave Background with Interacting Dark Matter and Dark Energy Models

Rui An, Chang Feng, Bin Wang

TL;DR

The paper addresses the discordance between KiDS-450 weak-lensing data and Planck CMB measurements within the standard $\Lambda$CDM framework by testing phenomenological interacting dark matter and dark energy models with $Q=3\lambda_{1}H\rho_{c}+3\lambda_{2}H\rho_{d}$. Using KiDS-450 tomographic weak-lensing spectra and Planck 2015 TT/TE/EE data, analyzed via Markov Chain Monte Carlo in CosmoMC, it evaluates model performance with the Deviance Information Criterion (DIC) and a tension metric $T(S_8)$. The joint KiDS+Planck analysis favors IDMDE over $\Lambda$CDM and substantially reduces the $S_8$ tension, with Model I implying DM→DE energy transfer and Models II–IV favoring DE→DM transfer, the latter helping alleviate the coincidence problem. The results suggest dark-sector interactions as a viable route to reconcile large-scale structure and CMB observations and motivate further multi-dataset analyses to constrain IDMDE scenarios.

Abstract

We constrain interacting dark matter and dark energy (IDMDE) models using a 450-degree-square cosmic shear data from the Kilo Degree Survey (KiDS) and the angular power spectra from Planck's latest cosmic microwave background measurements. We revisit the discordance problem in the standard Lambda cold dark matter ($Λ$CDM) model between weak lensing and Planck datasets and extend the discussion by introducing interacting dark sectors. The IDMDE models are found to be able to alleviate the discordance between KiDS and Planck as previously inferred from the $Λ$CDM model, and moderately favored by a combination of the two datasets.

Relieving the Tension between Weak Lensing and Cosmic Microwave Background with Interacting Dark Matter and Dark Energy Models

TL;DR

The paper addresses the discordance between KiDS-450 weak-lensing data and Planck CMB measurements within the standard CDM framework by testing phenomenological interacting dark matter and dark energy models with . Using KiDS-450 tomographic weak-lensing spectra and Planck 2015 TT/TE/EE data, analyzed via Markov Chain Monte Carlo in CosmoMC, it evaluates model performance with the Deviance Information Criterion (DIC) and a tension metric . The joint KiDS+Planck analysis favors IDMDE over CDM and substantially reduces the tension, with Model I implying DM→DE energy transfer and Models II–IV favoring DE→DM transfer, the latter helping alleviate the coincidence problem. The results suggest dark-sector interactions as a viable route to reconcile large-scale structure and CMB observations and motivate further multi-dataset analyses to constrain IDMDE scenarios.

Abstract

We constrain interacting dark matter and dark energy (IDMDE) models using a 450-degree-square cosmic shear data from the Kilo Degree Survey (KiDS) and the angular power spectra from Planck's latest cosmic microwave background measurements. We revisit the discordance problem in the standard Lambda cold dark matter (CDM) model between weak lensing and Planck datasets and extend the discussion by introducing interacting dark sectors. The IDMDE models are found to be able to alleviate the discordance between KiDS and Planck as previously inferred from the CDM model, and moderately favored by a combination of the two datasets.

Paper Structure

This paper contains 7 sections, 9 equations, 5 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Interacting dark matter and dark energy models
  3. The KiDS and Planck datasets
  4. Results
  5. Model selection
  6. Model constraints
  7. Conclusions

Figures (5)

  • Figure 1: Marginalized posterior distribution function in the $\sigma_8-\Omega_m$ plane for the $\Lambda$CDM model. $68\%$ and 95% confidence levels are shown as inner and outer regions.
  • Figure 2: 1D distributions of $S_8$ for KiDS and Planck.
  • Figure 3: Marginalized confidence contours in the $\sigma_8-\Omega_m$ plane for four IDMDE models. $68\%$ and 95% confidence levels are shown as inner and outer regions.
  • Figure 4: 1D likelihood functions for selected parameters
  • Figure 5: 2D posterior contours for selected parameters