اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدCharacterization of subhalo structural properties and implications for dark matter annihilation signals
221
0
0.0
(
0
)
تأليف
Angeles Moline
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
A prediction of the standard LCDM cosmology is that dark matter (DM) halos are teeming with numerous self-bound substructure, or subhalos. The precise properties of these subhalos represent important probes of the underlying cosmological model. We use data from Via Lactea II and ELVIS N-body simulations to learn about the structure of subhalos with masses 1e6 - 1e11 Msun/h. Thanks to a superb subhalo statistics, we study subhalo properties as a function of distance to host halo center and subhalo mass, and provide a set of fits that accurately describe the subhalo structure. We also investigate the role of subhalos on the search for DM annihilation. Previous work has shown that subhalos are expected to boost the DM signal of their host halos significantly. Yet, these works traditionally assumed that subhalos exhibit similar structural properties than those of field halos, while it is known that subhalos are more concentrated. Building upon our N-body data analysis, we refine the substructure boost model of Sanchez-Conde & Prada (2014), and find boosts that are a factor 2-3 higher. We further refine the model to include unavoidable tidal stripping effects on the subhalo population. For field halos, this introduces a moderate 20-30% suppression. Yet, for subhalos like those hosting dwarf galaxy satellites, tidal stripping plays a critical role, the boost being at the level of a few tens of percent at most. We provide a parametrization of the boost for field halos that can be safely applied over a wide halo mass range.
قيم البحث
اقرأ أيضاً
The cosmic electron and positron excesses have been explained as possible dark matter (DM) annihilation products. In this work we investigate the possible effects of such a DM annihilation scenario during the evolution history of the Universe. We fir
st calculate the extragalactic $gamma$-ray background (EGRB), which is produced through the final state radiation of DM annihilation to charged leptons and the inverse Compton scattering between electrons/positrons and the cosmic microwave background. The DM halo profile and the minimal halo mass, which are not yet well determined from the current N-body simulations, are constrained by the EGRB data from EGRET and Fermi telescopes. Then we discuss the impact of such leptonic DM models on cosmic evolution, such as the reionization and heating of intergalactic medium, neutral Hydrogen 21 cm signal and suppression of structure formation. We show that the impact on the Hydrogen 21 cm signal might show interesting signatures of DM annihilation, but the influence on star formation is not remarkable. Future observations of the 21 cm signals could be used to place new constraints on the properties of DM.
Warm dark matter has recently become increasingly constrained by observational inferences about the low-mass end of the subhalo mass function, which would be suppressed by dark matter free streaming in the early Universe. In this work, we point out t
hat a constraint can be placed on ultralight bosonic dark matter (often referred to as fuzzy dark matter) based on similar considerations. Recent limits on warm dark matter from strong gravitational lensing of quasars and from fluctuations in stellar streams separately translate to a lower limit of $sim 2.1 times 10^{-21}$ eV on the mass of an ultralight boson comprising all dark matter. These limits are complementary to constraints on ultralight dark matter from the Lyman-$alpha$ forest and are subject to a completely different set of assumptions and systematic uncertainties. Taken together, these probes strongly suggest that dark matter with a mass $sim 10^{-22}$ eV is not a viable way to reconcile differences between cold dark matter simulations and observations of structure on small scales.
Cosmological N-body simulations show that Milky-Way-sized galaxies harbor a population of unmerged dark matter subhalos. These subhalos could shine in gamma rays and be eventually detected in gamma-ray surveys as unidentified sources. We search for v
ery-high-energy (VHE, $Egeq 100$ GeV) gamma-ray emission using H.E.S.S. observations carried out from a thorough selection of unidentified Fermi-LAT Objects (UFOs) as dark matter subhalo candidates. Provided that the dark matter mass is higher than a few hundred GeV, the emission of the UFOs can be well described by dark matter annihilation models. No significant VHE gamma-ray emission is detected in any UFO dataset nor in their combination. We, therefore, derive constraints on the product of the velocity-weighted annihilation cross-section $langle sigma vrangle$ by the $J$-factor on dark matter models describing the UFO emissions. Upper limits at 95% confidence level are derived on $langle sigma vrangle J$ in $W^+W^-$ and $tau^+tau^-$ annihilation channels for the TeV dark matter particles. Focusing on thermal WIMPs, strong constraints on the $J$-factors are obtained from H.E.S.S. observations. Adopting model-dependent predictions from cosmological N-body simulations on the $J$-factor distribution function for Milky Way (MW)-sized galaxies, only $lesssim 0.3$ TeV mass dark matter models marginally allow to explain observed UFO emission.
We are at the dawn of a data-driven era in astrophysics and cosmology. A large number of ongoing and forthcoming experiments combined with an increasingly open approach to data availability offer great potential in unlocking some of the deepest myste
ries of the Universe. Among these is understanding the nature of dark matter (DM)---one of the major unsolved problems in particle physics. Characterizing DM through its astrophysical signatures will require a robust understanding of its distribution in the sky and the use of novel statistical methods. The first part of this thesis describes the implementation of a novel statistical technique which leverages the clumpiness of photons originating from point sources (PSs) to derive the properties of PS populations hidden in astrophysical datasets. This is applied to data from the Fermi satellite at high latitudes ($|b| > 30$deg) to characterize the contribution of PSs of extragalactic origin. We find that the majority of extragalactic gamma-ray emission can be ascribed to unresolved PSs having properties consistent with known sources such as active galactic nuclei. This leaves considerably less room for significant dark matter contribution. The second part of this thesis poses the question: what is the best way to look for annihilating dark matter in extragalactic sources? and attempts to answer it by constructing a pipeline to robustly map out the distribution of dark matter outside the Milky Way using galaxy group catalogs. This framework is then applied to Fermi data and existing group catalogs to search for annihilating dark matter in extragalactic galaxies and clusters.
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particl
e physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. Through proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
