Halo spin and orientation in Interacting Dark Matter Dark Energy Cosmology

TL;DR

This study investigates how interacting dark matter–dark energy (IDE) cosmologies modify halo spin and shape alignments with the surrounding tidal field, using the ME-GADGET N-body pipeline. IDE is modeled via a phenomenological energy transfer $Q(\rho_M,\rho_E)=\xi_1\rho_M+\xi_2\rho_E$, with a focus on the parameter $\xi_2$ and two regimes: IDE I (DM decay) and IDE II (DM growth), which alter DM density evolution and halo properties. The authors quantify in-situ spin–tidal and shape–tidal correlations and their spatial cross-correlations, providing fitted functions $\eta_{LT}(M) = a x + b$, $\eta_{GT}(M) = a x^{b}$, and several correlation-function forms to enable IA calibration for current and future surveys such as CSST. The results show dissipation enhances shape–tidal and reduces spin–tidal alignment (IDE I), while proliferation strengthens spin–tidal and weakens shape–tidal alignment (IDE II), with tidal–tidal autocorrelations largely unchanged; these findings offer concrete inputs for modeling halo IA signals under IDE and for interpreting weak-lensing observations.

Abstract

In recent years, the interaction between dark matter (DM) and dark energy (DE) has become a topic of interest in cosmology. Interacting dark matter-dark energy (IDE) models have a substantial impact on the formation of cosmological large-scale structures, which serve as the background for DM halo evolution. This impact can be examined through the shape and spin orientation of halos in numerical simulations incorporating IDE effects. In our work, we use the N-body simulation pipeline ME-GADGET to simulate and study the halo spin and orientation in IDE models. We found that in models where DM transfers into DE (IDE I), the alignment of halo shapes with the surrounding tidal field is enhanced, while the alignment of halo spins with the tidal field is decreased compared to $Λ$CDM. Conversely, in models where DE transfers into DM (IDE II), the opposite occurs. We have provided fitted functions to describe these alignment signals. Our study provides the foundation for more accurate modeling of observations in the future such as China Space Station Telescope.

Figures (6)

• Figure 1: Mass of DM particles at different redshift in IDE I, $\Lambda$CDM and IDE II, respectively. Note that the difference between IDE models and $\Lambda$CDM is only significant at $z<4$, which highlights the low-redshift property of the DM-DE interaction.
• Figure 2: Halo mass functions at redshift 0 in IDE I, $\Lambda$CDM and IDE II respectively. The total number of halos in IDE I is significantly less than $\Lambda$CDM due to DM decay.
• Figure 3: The figure shows simulated particle snapshots (first row), density heightmap (second row), and the tidal field direction $\vec{T}$ (third row) in models IDE I, $\Lambda$CDM and IDE II. First row: the simulation particle snapshots at redshift 0 (black point cloud), and the AHF halos (orange circles with radius representing the viral radius $R_{vir}$). Second row: density heatmap at overdensity $\delta>0$. Third row: the tidal tensor least eigenvalue arrow plot in dense regions, with coloring denoting the inclination angle above the x-y plane.
• Figure 4: This figure illustrates the evolution of a single halo in its large-scale environment. From left to right are the snapshots from $z=3.62$ to $z=0.0$. The width and the height of the snapshots are both $10Mpc/h$, and the depth of the snapshots is $10Mpc/h$. Compared to $\Lambda CDM$, the halos in IDE I are more vulnerable against merging and the tidal environment due to its loose structure, while the halos in IDE II are more robust.
• Figure 5: The figure shows halo alignment strength vs halo virial mass. Left panel: halo spin-tidal alignment, linear fit lines are plotted as dashed lines. Right panel: halo shape-tidal alignment, powerlaw fit curves are plotted as the dashed lines. The best-fit parameters and fit forms are listed in Table \ref{['tab:insitu_corr_fits']}.