New insights on low-mass dark matter subhalo tidal tracks via numerical simulations

TL;DR

The paper addresses how low-mass dark matter subhaloes evolve under tidal stripping within a time-evolving Milky Way–like potential that includes baryons. Using high-resolution DASH simulations, it develops tidal tracks for structural parameters $V__\mathrm{max}$ and $r__\mathrm{max}$, and for the velocity concentration $c__\mathrm{V}$, for both NFW ($\gamma=1$) and prompt-cusp ($\gamma=1.5$) inner profiles, and at both apocentres and pericentres. It finds that $r__\mathrm{max}$ shrinks faster than $V__\mathrm{max}$, driving a significant rise in concentration (up to ~2 dex) and that $c__\mathrm{V}$ increases substantially from infall to present, with pericentre tracks generally showing stronger effects; the study provides new analytic fits and highlights the role of accretion redshift and orbital parameters in shaping these tracks. These results improve our understanding of subhalo populations in the MW context and have implications for interpreting satellite dynamics, gravitational lensing, stellar streams, and indirect dark matter searches, while acknowledging limitations from the lack of hydrodynamical feedback and finite resolution.

Abstract

Many studies assert that dark matter (DM) subhaloes without a baryonic counterpart and with an inner cusp always survive no matter the strength of the tidal force they undergo. In this work, we perform a suite of numerical simulations specifically designed to analyse the evolution of $V_\mathrm{max}$, $r_\mathrm{max}$ and concentration of low-mass DM subhaloes due to tidal stripping. We employ the improved version of the DASH code, introduced in our previous work arXiv:2207.08652 to investigate subhalo survival. We follow the tidal evolution of a single DM subhalo orbiting a Milky Way (MW)-size halo modeled with a baryonic disc and a bulge replicating the actual mass distribution of the MW. We consider the effect of the time-evolving gravitational potential of the MW itself. We simulate subhaloes with unprecedented accuracy, varying their initial concentration, orbital parameters, and inner slope (both NFW and prompt cusps are considered). Unlike the previous literature, we examine the evolution of subhalo structural parameters -- tidal tracks -- not only at orbit apocentres but also at pericentres, finding in the former case both similarities and differences -- particularly pronounced in the case of prompt cusps. Overall, $r_\mathrm{max}$ shrinks more than $V_\mathrm{max}$, leading to a continuous rise of subhalo concentration with time. The velocity concentration at present is found to be around two orders of magnitude higher than the one at infall, being comparatively larger for pericentre tidal tracks versus apocentres. These findings highlight the dominant role of tidal effects in reshaping low-mass DM subhaloes, providing valuable insights for future research via simulations and observations, such as correctly interpreting data from galaxy satellite populations, subhalo searches with gravitational lensing or stellar stream analyses, and indirect DM searches.

Figures (19)

• Figure 1: Circular velocities for each snapshot in a simulation with our fiducial parameters reported in Table \ref{['tab:fiduset']}. The $x$ axis is the radius normalised to the subhalo virial radius at accretion. Colour represents time, namely snapshot number, being yellow the beginning of the run, down to the purple at the bottom corresponding to $z=0$. Circles are drawn for the $V_{\mathrm{max}}$ found after every 10 snapshots, while lines every 20 snapshots are labeled in the legend and highlighted in dash-dotted and thicker layout. The dashed vertical line corresponds to $R_\mathrm{sub,vir}$.
• Figure 2: Evolution of $c_\mathrm{V}$ as a function of $V_\mathrm{max}$ (filled stars) and time (squares) for our fiducial run (Table \ref{['tab:fiduset']}). Colour indicates the bound mass fraction $f_\mathrm{b}$, with yellow denoting the beginning of the simulation. Black hollow markers are drawn after every 10 snapshots.
• Figure 3: Evolution of $V_\mathrm{max}$ and $r_\mathrm{max}$ normalised to their initial values throughout the whole life of a subhalo since its accretion (yellow) until present day (purple). Top panel: Each quantity against the time (lower axis) or redshift (upper axis). Bottom panel: One quantity against the other. Apocentres are highlighted as aquamarine hollow triangles while pericentres appear as red and inverted. Changes occur near the pericentres. $V_\mathrm{max}$ decreases later than $r_\mathrm{max}$. Besides, $r_\mathrm{max}$ decreases more. See main text for details.
• Figure 4: Top panel: Evolution of $V_\mathrm{max}$ (filled markers) and $r_\mathrm{max}$ (hollow markers) as a function of the scale factor $a$, subtracting the value of the respective pericentre, throughout a single orbital period. Only the two first orbits and the last one are shown, with black circles, blue stars and cyan diamonds, respectively. Values are initialised at the first value of the respective orbit, which is the beginning of the simulation in the former case, and the apocentre in subsequent ones. Dotted curves show the evolution of the distance between the subhalo and the host halo centre for the three mentioned orbits, adopting the same colour scheme and normalised by the virial radius of the host at present. Bottom panel: Evolution of $c_\mathrm{V}$, showing an increase of the velocity concentration throughout each orbit, which is more significant at the beginning.
• Figure 5: Relation between $f_\mathrm{b}$ and $V_\mathrm{max}/V_\mathrm{max,i}$, for the apocentres (green stars) and the pericentres (sky blue diamonds). Subhaloes have an NFW density profile at accretion. The tidal track found for each subset using Eq. \ref{['eq:tidalP10']} is drawn as a solid green line in the former case and a dashed sky blue line in the latter, with shadowed bands for their respective scatter. The fits from Penarrubia2010 (loosely dashed yellow line) and 2024PhRvD.110b3019D (purple dotted line) are included, and thinned when they are extrapolated. Data can be trusted to the right of the vertical dotted line. We have adjoined a lower panel with the difference between our best fit for the apocentres and the literature fits, calculated as $\log_{10}$ [our fit using apocentres] $- \log_{10}$ [fit in the literature].