Structure of Dark Matter Halos from Hierarchical Clustering. III. Shallowing of the Inner Cusp

Fukushige, Toshiyuki; Kawai, Maki; Makino, Junichiro

doi:10.1086/383192

Cited by 100 publications

(118 citation statements)

References 24 publications

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“…The most recent simulations [24,25] (see also Refs. [26,27]) suggest a power-law index that varies slowly with radius, but the normalization and slope of these models at r ≈ r h are essentially identical to those of models with an unbroken, ρ ∝ r −1 power law inward of the Sun. We took R ⊙ = 8.0 kpc for the radius of the Solar circle [28].…”

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confidence: 81%

Time-dependent models for dark matter at the galactic center

2005

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The prospects for indirect detection of dark matter at the Galactic center with gamma-ray experiments like the space telescope GLAST, and Air Cherenkov Telescopes like HESS, CANGAROO, MAGIC and VERITAS, depend sensitively on the mass profile within the inner parsec. We calculate the distribution of dark matter on sub-parsec scales by integrating the time-dependent Fokker-Planck equation, including the effects of self-annihilations, scattering of dark matter particles by stars, and capture in the supermassive black hole. We consider a variety of initial dark matter distributions, including models with very high densities ("spikes") near the black hole, and models with "adiabatic compression" of the baryons. The annihilation signal after 10 10 yr is found to be substantially reduced from its initial value, but in dark matter models with an initial spike, order-of-magnitude enhancements can persist compared with the rate in spike-free models. PACS numbers:FERMILAB-PUB-05-013-AThere is compelling evidence that the matter density of the Universe is dominated by some sort of non-baryonic, "dark", matter, the best candidates being weakly interacting massive particles [1,2]. Numerical N -body simulations suggest dark matter (DM) density profiles following broken power laws, ρ ∝ r −γ , with γ ≈ 3 in the outer parts of halos and 1 < ∼ γ < ∼ 1.5 ("cusps") inside the Solar circle. Although these profiles reproduce with sufficently good accuracy the observed properties of galactic halos on large scales, as inferred by rotation curves, little is known about the DM distribution on smaller scales, where the gravitational potential is dominated by baryons. The situation at the Galactic center (GC) is further complicated by the presence of a supermassive black hole (SBH), with mass ∼ 10 6.5 M ⊙ [3], whose sphere of gravitational influence extends out to ∼ 1 pc.The prospects for indirect detection depend crucially on the distribution of DM within this small region. The flux of gamma-rays from the GC, from the annihilation of DM particles of mass m and annihilation cross section in the non-relativistic limit σv, can be written:where Φ 0 = 5.6 × 10 −12 cm −2 s −1 and and σv th = 3 × 10 −26 cm 3 s −1 is the value of the thermally averaged cross section at decoupling that reproduces the observed cosmological abundance of Dark Matter (although in presence of resonance effects like co-annihilations, the correct relic abundance can be achieved with smaller cross sections). For more details and a review on DM candidates and detection see e.g. Refs. [1,2]. J ∆Ω is a factor containing all the information on the DM profile [4]:where dl is the distance element along the line of sight at angle ψ with respect to the GC, ∆Ω is the solid angle of the detector, and K is a normalizing factor, K −1 = (8.5kpc)(0.3GeV/cm 3 ) 2 . We denote by J 5 and J 3 the values of J when ∆Ω = 10 −5 sr and 10 −3 sr respectively; the former is the approximate field of view of the detectors in GLAST [5] and in atmospheric Cerenkov telescopes like VERITAS [6] and HES...

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confidence: 81%

Time-dependent models for dark matter at the galactic center

2005

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“…Many groups have studied the density profile of dark matter halos using high-resolution cosmological N-body simulations (e.g., Navarro et al 1997Navarro et al , 2004Navarro et al , 2010Fukushige & Makino 1997, 2001Moore et al 1999b;Ghigna et al 2000;Jing & Suto 2000Jing 2000;Klypin et al 2001;Taylor & Navarro 2001;Power et al 2003;Fukushige et al 2004;Diemand et al 2004Diemand et al , 2005Diemand et al , 2008Hayashi et al 2004;Reed et al 2005;Kazantzidis et al 2006;Merritt et al 2006;Gao et al 2008;Stadel et al 2009). In most of the recent works, the slopes of radial density profiles were around −1 in the inner region and around −3 in the outer region.…”

Section: Density Structures Of Most Massive Halosmentioning

confidence: 99%

The Cosmogrid Simulation: Statistical Properties of Small Dark Matter Halos

Ishiyama

Rieder

Makino

et al. 2013

ApJ

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101

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We present the results of the "Cosmogrid" cosmological N-body simulation suites based on the concordance LCDM model. The Cosmogrid simulation was performed in a 30 Mpc box with 2048 3 particles. The mass of each particle is 1.28 × 10 5 M , which is sufficient to resolve ultra-faint dwarfs. We found that the halo mass function shows good agreement with the Sheth & Tormen fitting function down to ∼10 7 M . We have analyzed the spherically averaged density profiles of the three most massive halos which are of galaxy group size and contain at least 170 million particles. The slopes of these density profiles become shallower than −1 at the innermost radius. We also find a clear correlation of halo concentration with mass. The mass dependence of the concentration parameter cannot be expressed by a single power law, however a simple model based on the Press-Schechter theory proposed by Navarro et al. gives reasonable agreement with this dependence. The spin parameter does not show a correlation with the halo mass. The probability distribution functions for both concentration and spin are well fitted by the log-normal distribution for halos with the masses larger than ∼10 8 M . The subhalo abundance depends on the halo mass. Galaxy-sized halos have 50% more subhalos than ∼10 11 M halos have.

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“…It is important to note that the Z05 prescription for mass loss does not include the effects that baryons can have on dark matter. Adiabatic Contraction (AC; Blumenthal et al 1986;Ryden & Gunn 1987;Gnedin et al 2004) may increase the central density as the gas condenses and sinks to the center of the dark matter potential well (Diemand et al 2004;Fukushige et al 2004;Reed et al 2005;Del Popolo & Kroupa 2009). Conversely, processes during halo formation, such as gravitational heating from merger events, can counteract AC (e.g., Zappacosta et al 2006) and, in fact, the central density may decrease through baryonic feedback (Governato et al 2012).…”

Section: Models For Satellite Galaxy Stellar Mass Lossmentioning

confidence: 99%

Constraining Satellite Galaxy Stellar Mass Loss and Predicting Intrahalo Light. I. Framework and Results at Low Redshift

Watson¹,

Berlind²,

Zentner³

2012

ApJ

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We introduce a new technique that uses galaxy clustering to constrain how satellite galaxies lose stellar mass and contribute to the diffuse "intrahalo light" (IHL). We implement two models that relate satellite galaxy stellar mass loss to the detailed knowledge of subhalo dark matter mass loss. Model 1 assumes that the fractional stellar mass loss of a galaxy, from the time of merging into a larger halo until the final redshift, is proportional to the fractional amount of dark matter mass loss of the subhalo it lives in. Model 2 accounts for a delay in the time that stellar mass is lost due to the fact that the galaxy resides deep in the potential well of the subhalo and the subhalo may experience dark matter mass loss for some time before the galaxy is affected. We use these models to predict the stellar masses of a population of galaxies and we use abundance matching to predict the clustering of several r-band luminosity threshold samples from the Sloan Digital Sky Survey. Abundance matching assuming no stellar mass loss (akin to abundance matching at the time of subhalo infall) overestimates the correlation function on small scales ( 1 Mpc), while allowing too much stellar mass loss leads to an underestimate of small-scale clustering. For each luminosity threshold sample, we are thus able to constrain the amount of stellar mass loss required to match the observed clustering. We find that satellite galaxy stellar mass loss is strongly luminosity dependent, with less luminous satellite galaxies experiencing substantially more efficient stellar mass loss than luminous satellites. With constrained stellar mass loss models, we can infer the amount of stellar mass that is deposited into the IHL. We find that both of our model predictions for the mean amount of IHL as a function of halo mass are consistent with current observational measurements. However, our two models predict a different amount of scatter in the IHL from halo to halo, with Model 2 being favored by observations. This demonstrates that a comparison to IHL measurements provides independent verification of our stellar mass loss models, as well as additional constraining power.

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Structure of Dark Matter Halos from Hierarchical Clustering. III. Shallowing of the Inner Cusp

Cited by 100 publications

References 24 publications

Time-dependent models for dark matter at the galactic center

Time-dependent models for dark matter at the galactic center

The Cosmogrid Simulation: Statistical Properties of Small Dark Matter Halos

Constraining Satellite Galaxy Stellar Mass Loss and Predicting Intrahalo Light. I. Framework and Results at Low Redshift

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