Self-interacting dark matter (SIDM) offers the potential to mitigate some of the discrepancies between simulated cold dark matter (CDM) and observed galactic properties. We introduce a physically motivated SIDM model to understand the effects of self interactions on the properties of Milky Way and dwarf galaxy sized haloes. This model consists of dark matter with a nearly degenerate excited state, which allows for both elastic and inelastic scattering. In particular, the model includes a significant probability for particles to up-scatter from the ground state to the excited state. We simulate a suite of zoom-in Milky Way-sized N-body haloes with six models with different scattering cross sections to study the effects of up-scattering in SIDM models. We find that the up-scattering reaction greatly increases the central densities of the main halo through the loss of kinetic energy. However, the physical model still results in significant coring due to the presence of elastic scattering and down-scattering. These effects are not as apparent in the subhalo population compared to the main halo, but the number of subhaloes is reduced compared to CDM.
Dark matter haloes have long been recognized as one of the fundamental building blocks of large-scale structure formation models. Despite their importance – or perhaps because of it! – halo definitions continue to evolve towards more physically motivated criteria. Here, we propose a new definition that is physically motivated, effectively unique, and parameter-free: ‘A dark matter halo is comprised of the collection of particles orbiting in their own self-generated potential’. This definition is enabled by the fact that, even with as few as ≈300 particles per halo, nearly every particle in the vicinity of a halo can be uniquely classified as either orbiting or infalling based on its dynamical history. For brevity, we refer to haloes selected in this way as physical haloes. We demonstrate that (1) the mass function of physical haloes is Press–Schechter, provided the critical threshold for collapse is allowed to vary slowly with peak height; and (2) the peak-background split prediction of the clustering amplitude of physical haloes is statistically consistent with the simulation data, with accuracy no worse than ≈5 per cent.
more » « less- PAR ID:
- 10402132
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 521
- Issue:
- 2
- ISSN:
- 0035-8711
- Format(s):
- Medium: X Size: p. 2464-2476
- Size(s):
- p. 2464-2476
- Sponsoring Org:
- National Science Foundation
More Like this
-
ABSTRACT -
null (Ed.)ABSTRACT The splashback radius, Rsp, is a physically motivated halo boundary that separates infalling and collapsed matter of haloes. We study Rsp in the hydrodynamic and dark matter-only IllustrisTNG simulations. The most commonly adopted signature of Rsp is the radius at which the radial density profiles are steepest. Therefore, we explicitly optimize our density profile fit to the profile slope and find that this leads to a $\sim 5{{\ \rm per\ cent}}$ larger radius compared to other optimizations. We calculate Rsp for haloes with masses between 1013 and 15 M⊙ as a function of halo mass, accretion rate, and redshift. Rsp decreases with mass and with redshift for haloes of similar M200 m in agreement with previous work. We also find that Rsp/R200 m decreases with halo accretion rate. We apply our analysis to dark matter, gas, and satellite galaxies associated with haloes to investigate the observational potential of Rsp. The radius of steepest slope in gas profiles is consistently smaller than the value calculated from dark matter profiles. The steepest slope in galaxy profiles, which are often used in observations, tends to agree with dark matter profiles but is lower for less massive haloes. We compare Rsp in hydrodynamic and N-body dark matter-only simulations and do not find a significant difference caused by the addition of baryonic physics. Thus, results from dark matter-only simulations should be applicable to realistic haloes.more » « less
-
ABSTRACT We build a deep learning framework that connects the local formation process of dark matter haloes to the halo bias. We train a convolutional neural network (CNN) to predict the final mass and concentration of dark matter haloes from the initial conditions. The CNN is then used as a surrogate model to derive the response of the haloes’ mass and concentration to long-wavelength perturbations in the initial conditions, and consequently the halo bias parameters following the ‘response bias’ definition. The CNN correctly predicts how the local properties of dark matter haloes respond to changes in the large-scale environment, despite no explicit knowledge of halo bias being provided during training. We show that the CNN recovers the known trends for the linear and second-order density bias parameters b1 and b2, as well as for the local primordial non-Gaussianity linear bias parameter bϕ. The expected secondary assembly bias dependence on halo concentration is also recovered by the CNN: at fixed mass, halo concentration has only a mild impact on b1, but a strong impact on bϕ. Our framework opens a new window for discovering which physical aspects of the halo’s Lagrangian patch determine assembly bias, which in turn can inform physical models of halo formation and bias.
-
Varying primordial state fractions in exo- and endothermic SIDM simulations of Milky Way-mass haloes
ABSTRACT Self-interacting dark matter (SIDM) is increasingly studied as a potential solution to small-scale discrepancies between simulations of cold dark matter (CDM) and observations. We examine a physically motivated two-state SIDM model with both elastic and inelastic scatterings. In particular, endothermic, exothermic, and elastic scattering have equal transfer cross-sections at high relative velocities ($v_{\rm rel}\gtrsim 400~{\rm km\, s}^{-1})$. In a suite of cosmological zoom-in simulation of Milky Way-size haloes, we vary the primordial state fractions to understand the impact of inelastic dark matter self-interactions on halo structure and evolution. In particular, we test how the initial conditions impact the present-day properties of dark matter haloes. Depending on the primordial state fraction, scattering reactions will be dominated by either exothermic or endothermic effects for high and low initial excited state fractions, respectively. We find that increasing the initial excited fraction reduces the mass of the main halo, as well as the number of subhaloes on all mass scales. The main haloes are cored, with lower inner densities and higher outer densities compared with CDM. Additionally, we find that the shape of the main halo becomes more spherical the higher the initial excited state fraction is. Finally, we show that the number of satellites steadily decreases with initial excited state fraction across all satellite masses.
-
ABSTRACT Dark matter subhaloes are key for the predictions of simulations of structure formation, but their existence frequently ends prematurely due to two technical issues, namely numerical disruption in N-body simulations and halo finders failing to identify them. Here, we focus on the second issue, using the phase-space friends-of-friends halo finder Rockstar as a benchmark (though we expect our results to translate to comparable codes). We confirm that the most prominent cause for losing track of subhaloes is tidal distortion rather than a low number of particles. As a solution, we present a flexible post-processing algorithm that tracks all subhalo particles over time, computes subhalo positions and masses based on those particles, and progressively removes stripped matter. If a subhalo is lost by the halo finder, this algorithm keeps tracking its so-called ghost until it has almost no particles left or has truly merged with its host. We apply this technique to a large suite of N-body simulations and restore lost subhaloes to the halo catalogues, which has a substantial effect on key summary statistics of large-scale structure. Specifically, the subhalo mass function increases by about 20 per cent to 30 per cent and the halo correlation function by about 50 per cent at small scales. While these quantitative results are somewhat specific to our algorithm, they demonstrate that particle tracking is a promising way to reliably follow haloes and to reduce the need for orphan models. Our algorithm and augmented halo catalogues are publicly available.