Modeling evolution of dark matter substructure and annihilation boost

Abstract

We study evolution of dark matter substructures, especially how they lose the mass and change density profile after they fall in gravitational potential of larger host halos. We develop an analytical prescription that models the subhalo mass evolution and calibrate it to results of N -body numerical simulations of various scales from very small (Earth size) to large (galaxies to clusters) halos. We then combine the results with halo accretion histories, and calculate the subhalo mass function that is physically motivated down to Earth-mass scales. Our results -valid for arbitrary host masses and redshifts -show reasonable agreement with those of numerical simulations at resolved scales. Our analytical model also enables self-consistent calculations of the boost factor of dark matter annhilation, which we find to increase from tens of percent at the smallest (Earth) and intermediate (dwarfs) masses to a factor of several at galaxy size, and to become as large as a factor of ∼10 for the largest halos (clusters) at small redshifts. Our analytical approach can accommodate substructures in the subhalos (sub-subhalos) in a consistent framework, which we find to give up to a factor of a few enhancement to the annihilation boost. Presence of the subhalos enhances the intensity of the isotropic gamma-ray background by a factor of a few, and as the result, the measurement by Fermi Large Area Telescope excludes the annihilation cross section greater than ∼4 × 10 −26 cm 3 s −1 for dark matter masses up to ∼200 GeV.

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

“…is additionally described inHan et al (2016);Despali & Vegetti (2017). In our fiducial setups, we take the minimum and maximum subhalo masses to be m 200,min = 10 6 M and m 200,max = 0.01 M 200(Despali & Vegetti 2017;Hiroshima et al 2018) respectively, and corresponding fiducial substructure mass fraction in this range of 5%, roughly consistent with observations inDalal & Kochanek (2002);Hiroshima et al (2018);Hsueh et al (2019).…”

Impact of substructure on local dark matter searches

“…This prescription was calibrated against the results of N -body simulations [72], allowing the method to cover sub-halo masses from ∼ 10 11 M down to ∼ 10 −6 M , much smaller than those that can be resolved in the corresponding N -body simulations. We use tabulated halo mass functions and sub-halo properties provided by the authors of [31], though we note that fitting functions are also provided in Appendix A of Ref. [70].…”

“…This mass function may be written in the form dN/dM ∝ M −α(M ) , with α(M ) a mass-dependent parameter which ranges between 1.8 and 2.0 over the relevant mass range, as shown in the right panel. We also show in figure 1 the halo mass function and α(M ) for two parametric models with dN/dM ∝ M −1.8 and dN/dM ∝ M −2.0 , where the normalization of the power-law is fit to the results of [31]. The low mass sub-halos predicted in all of these models may have a significant impact in the search for dark matter inside the Solar System, as we will analyze in sections 3 and 4.…”

“…More concretely, the probability of finding a sub-halo of mass M is P (M ) = 1 N sh dN/dM , with dN/dM shown in figure 1, and N sh the total number of sub-halos, given in [31]. The distribution of concentrations c V for sub-halos of mass M , P (c V |M ), is obtained using the tabulated results of Ref.…”

“…The distribution of concentrations c V for sub-halos of mass M , P (c V |M ), is obtained using the tabulated results of Ref. [31]. Lastly, and assuming for simplicity that the spatial distribution of sub-halos is independent of their mass and concentration, the probability of finding a sub-halo center at a distance D from the Earth can be estimated as:…”