Impact of baryons on the population of Galactic subhalos and implications for dark matter searches

TL;DR

The paper investigates how baryonic physics alters the Galactic subhalo population in Milky-Way-like halos by comparing Auriga hydrodynamic and DM-only runs and by repopulating unresolved low-mass subhalos. It characterizes subhalos through SHVF, SRD, and velocity concentration $c_V$ derived from Auriga, then extrapolates below resolution with four repopulation scenarios (fragile/resilient × DMO/MHD) across 500 realizations, computing their J-factors for gamma-ray DM searches. The results show baryons reduce subhalo abundance by about a factor of $2.4$ (fragile) or $1.9$ (resilient) and decrease concentration by ~1.5 relative to DM-only, leading to weaker DM annihilation constraints in dark-satellite scenarios; however, higher resilience can substantially tighten those limits. The study underscores the necessity of including baryons for realistic subhalo characterizations and highlights implications for DM searches via gamma rays and gravitational signatures such as stellar streams and lensing.

Abstract

We have used Auriga -- a set of state-of-the-art cosmological hydrodynamical simulations of Milky Way-size systems -- to study the impact of baryons on the Galactic subhalo population. A DM-only run counterpart of Auriga allows us to compare results with and without baryons. We repopulate the original suites with low-mass subhalos orders of magnitude lighter than the mass resolution limit, starting from a detailed characterization of Auriga data in the well-resolved subhalo mass range. The survival of low-mass subhalos to tidal forces is unclear and under debate nowadays, thus in our study we stay agnostic and consider two different levels of subhalo resilience to tidal stripping ('fragile' and 'resilient' subhalos). We find baryons to alter the Galactic substructure significantly, by decreasing its overall abundance by a factor $\sim2.4$ (fragile) and $\sim1.9$ (resilient) and subhalo concentration -- here defined in terms of maximum circular velocity -- by $\sim1.5$ with respect to the DM-only scenario. This has important consequences for indirect searches of DM. As an example, we investigated the case of using unidentified gamma-ray sources to set constraints on the DM particle properties, assuming some of them may be dark satellites. We find the DM annihilation cross-section constraints to worsen by a factor $\sim3.6$ in the most realistic scenario of including baryons, compared to DM-only simulations in the 'fragile' setup. Yet, a stronger resilience of subhalos to tidal stripping improves these DM limits by a factor $\sim4.5$ and $\sim10$ compared to the DM-only and hydrodynamical 'fragile' cases, respectively. Our results show the importance of including baryons to properly characterize the Galactic subhalo population, as well as to propose the most optimal subhalo search strategies, not only via its potential DM annihilation products but also through their gravitational signatures.

Figures (18)

• Figure 1: Pair of $V_\mathrm{max}$ and $R_\mathrm{max}$ values of subhalos in the six Auriga MW-like halos analyzed in this work. Data from both DMO (black points) and MHD (green) runs are shown. The dotted vertical line depicts the separation we adopt between those subhalos expected to host dwarf galaxies ('dwarfs') and those that remain dark ('dark satellites'). Full details are given in Section \ref{['sect:auriga_characterization']}. The horizontal line at the bottom refers to the Auriga softening length for this level of resolution, i.e. 188 pc.
• Figure 2: Average SHVF from Auriga data as derived from the six MW-size halos we use in this work, both for DMO (black markers) and MHD (green markers) runs. Power law fits to Eq. \ref{['eq:shvf']} applying bootstrapping techniques are shown as solid lines, with best-fit parameters listed in Table \ref{['tab:bestParams']}. The vertical dotted lines refer to the corresponding $V_\mathrm{cut}$ values, the simulation lacking subhalos due to numerical resolution to the left of these lines.
• Figure 3: Left: Auriga SRD (data points), derived for subhalos with $V_\mathrm{max} > V_\mathrm{uni}$; see Table \ref{['tab:bestParams']} and Section \ref{['subsect:srd']} for details. Dashed lines are the best fits to the SRD data as given by Eq. \ref{['eq:srd_original']} for the fragile scenario. Also shown are the SRDs for the resilient scenario, Eq. \ref{['eq:srd_resilient']} (dotted lines), both for the DMO (black) and MHD (green) populations. Best-fit values for all these equations are given in Table \ref{['tab:bestParams']}. The galactocentric distance of the Earth, $R_\oplus=8.5\,$kpc, is also displayed as a vertical orange dash-dotted line (where we have assumed the virial radius of the MW from observations, $R_\mathrm{vir} = 220\,$kpc Klypin_2002, to transform its position into the plot units). Right: Same elements than in the left translated to the density space, dividing the subhalo number by the volume associated with the bins. The obtained curves do not have analytical formulas in the density space.
• Figure 4: Left: Geometric mean values of $c_\mathrm{V}$ as a function of $V_\mathrm{max}$ as found in Auriga for both DMO and MHD scenarios (black and green markers, respectively). The velocity concentration model for DMO by Moline21 at $z=0$ is shown as a dotted red line, with the expression and parameters given in Eq. \ref{['eq:cv']}. We have fitted Auriga data to the same parametric form at $V_\mathrm{max} \geq V_\mathrm{res}$, the latter shown as a dashed vertical line. Our fits to actual data are shown as straight lines while their extrapolations to smaller $V_\mathrm{max}$ appear as dashed lines. The best-fit values are provided in Table \ref{['tab:bestParams']}. Shadowed regions depict 1$\sigma$ scatter from the mean values. Right: Histogram of individual $c_\mathrm{V}$ values for those data over the resolution limit in the left figure. Opaque stripped regions contain the 1$\sigma$ intervals of the respective distributions. The vertical lines are the geometric mean values of the corresponding DMO (black) and MHD (green) distributions. Further explanations are found in the text.
• Figure 5: Flowchart of how the repopulation algorithm works; see Section \ref{['subsect:details_repop_algorithm']} for full details on each of the steps.