Distribution Of Dark Matter Halos Research Articles

Clustering of dark matter in the cosmic web as a probe of massive neutrinos

Abstract The large-scale structure of the Universe is distributed in a cosmic web. Studying the distribution and clustering of dark matter particles and halos may open up a new horizon for studying the physics of the dark Universe. In this work, we investigate the nearest neighbour statistics and spherical contact function in cosmological models with massive neutrinos. For this task, we use the relativistic N-body code, gevolution and study particle snapshots at three different redshifts. In each snapshot, we find the halos and evaluate the letter functions for them. We show that a generic behaviour can be found in the nearest neighbour, G(r), and spherical contact functions, F(r), which makes these statistics promising tools to constrain the total neutrino mass.

Secondary halo bias through cosmic time

Context. The spatial distribution of dark matter halos carries cosmological and astrophysical information. Cosmological information can be considered to be contained in the connection between halo main properties and the large-scale halo bias, while the astrophysical information would be encoded in the scaling relations between halo properties. The combination of these two contributions leads to the effect of secondary halo bias. Aims. Our goal is to measure the signal of secondary halo bias as a function of a variety of intrinsic and environmental halo properties and to characterize its statistical significance as a function of cosmological redshift. Methods. Using fixed and paired N-body simulations of dark-matter halos – the UNIT simulation – with masses above ∼1011 M⊙h−1 identified over a wide range of cosmological redshifts (0 < z < 5), we explored the behavior of the scaling relations among different halo properties. We included novel environmental properties based on the halo distribution as well as the underlying dark-matter field. We implemented an object-by-object estimator of large-scale effective bias and tested its validity against standard approaches. With a bias assigned to each tracer, we performed a statistical analysis aimed at characterizing the distribution of the bias and the signal of the secondary halo bias. Results. We show how the halo scaling relations linking direct probes of the halo potential well do not depend on the environment. On the contrary, links between the halo mass and the so-called set of secondary halo properties are sensitive to the cosmological environment, mainly to under-dense regions. We show that the signal of secondary bias is derived statistically from secondary correlations beyond the standard link to the halo mass. Conclusions. We show that the secondary bias arises through nonlocal and/or environmental properties related either to the halo distribution or to the properties of the underlying dark-matter field. In particular, properties such as the tidal field (a measure of the anisotropy of the density field) and the local Mach number (a measure of the local kinetic temperature of the halo distribution) generate the signals of the secondary bias with the highest significance. We propose applications of the assignment of individual bias for the generation of mock catalogs containing the signal of secondary bias, as well as a series of cosmological analyses aimed at mining large galaxy datasets.

Accurate halo mass functions from the simplest excursion set theory

ABSTRACT Excursion set theory is a powerful and widely used tool for describing the distribution of dark matter haloes, but it is normally applied with simplifying approximations. We use numerical sampling methods to study the mass functions predicted by the theory without approximations. With a spherical top-hat window and a constant δ = 1.5 threshold, the theory accurately predicts mass functions with the M200 mass definition, both unconditional and conditional, in simulations of a range of matter-dominated cosmologies. For Λ cold dark matter at the present epoch, predictions lie between the M200m and M200c mass functions. In contrast, with the same window function, a non-constant threshold based on ellipsoidal collapse predicts uniformly too few haloes. This work indicates a new way to simply and accurately evaluate halo mass functions, clustering bias, and assembly histories for a range of cosmologies. We provide a fitting function that accurately represents the predictions of the theory for a wide range of parameters.

Multi-stream radial structure of cold dark matter haloes from particle trajectories: deep inside splashback radius

Abstract By tracking trajectories of dark matter (DM) particles accreting onto haloes in cosmological N-body simulations, we investigate the radial phase-space distribution of cold dark matter (CDM) haloes, paying attention to their inner regions deep inside the halo boundary called the splashback radius, where the particles undergo multi-stream flows. Improving the analysis by Sugiura et al., we classify DM particles by the number of apocenter passages, p, and count it up to p = 40 for each halo over a wide mass range. Quantifying the radial density profile for particles having the same value of p, we find that it generally exhibits a double-power law feature, whose indices of inner and outer slopes are well-described by −1 and −8, respectively. Its characteristic scale and density are given as a simple fitting function of p, with a weak halo mass dependence. Interestingly, summing up these double-power law profiles beyond p = 40 reproduces well the total density profile of simulated haloes. The double-power law nature is persistent and generic not only in mass-selected haloes but also in haloes selected in different criteria. Our results are compared with self-similar solutions that describe the stationary and spherical accretion of DM. We find that even when introducing a non-zero angular momentum, none of them explain the radial multi-stream structure. The analysis with particle trajectories tracing back to higher redshifts suggests that the double-power law nature has been established during an early accretion phase and remains stable.

Topological data analysis reveals differences between simulated galaxies and dark matter haloes

ABSTRACT We use topological summaries based on Betti curves to characterize the large-scale spatial distribution of simulated dark matter haloes and galaxies. Using the IllustrisTNG and CAMELS-SAM simulations, we show that the topology of the galaxy distribution is significantly different from the topology of the dark matter halo distribution. Further, there are significant differences between the distributions of star-forming and quiescent galaxies. These topological differences are broadly consistent across all simulations, while at the same time there are noticeable differences when comparing between different models. Finally, using the CAMELS-SAM simulations, we show that the topology of the quiescent galaxies, in particular, depends strongly on the amount of supernova feedback. These results suggest that topological summary statistics could be used to help better understand the processes of galaxy formation and evolution.

DESI mock challenge

Context. We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. Aims. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). The construction of the catalogues shown in this paper is part of a mock-comparison project within the Dark Energy Spectroscopic Instrument (DESI) collaboration. Methods. We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed N-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. We introduce cosmic-web-dependent corrections to modelling redshift-space distortions at the N-body level – both in the halo and galaxy distributions –, as well as a multi-scale approach for accurate assignment of halo properties. Using specific models of halo occupation distributions to populate halos, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment. Results. BAM generates mock catalogues with per cent accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display ∼3%−10% accuracy in the multipoles of the power spectrum up to scales of k ∼ 0.4 h−1Mpc. We show that covariance matrices of two- and three-point statistics obtained with BAM display a similar structure to the reference simulation. Conclusions. BAM offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics, and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys.

Why cosmic voids matter: nonlinear structure & linear dynamics

We use the Magneticum suite of state-of-the-art hydrodynamical simulations to identify cosmic voids based on the watershed technique and investigate their most fundamental properties across different resolutions in mass and scale. This encompasses the distributions of void sizes, shapes, and content, as well as their radial density and velocity profiles traced by the distribution of cold dark matter particles and halos. We also study the impact of various tracer properties, such as their sparsity and mass, and the influence of void merging on these summary statistics. Our results reveal that all of the analyzed void properties are physically related to each other and describe universal characteristics that are largely independent of tracer type and resolution. Most notably, we find that the motion of tracers around void centers is perfectly consistent with linear dynamics, both for individual, as well as stacked voids. Despite the large range of scales accessible in our simulations, we are unable to identify the occurrence of nonlinear dynamics even inside voids of only a few Mpc in size. This suggests voids to be among the most pristine probes of cosmology down to scales that are commonly referred to as highly nonlinear in the field of large-scale structure.

Constraining the galaxy-halo connection of infrared-selected unWISE galaxies with galaxy clustering and galaxy-CMB lensing power spectra

We present the first detailed analysis of the connection between galaxies and their dark matter halos for the unWISE galaxy catalog -- a full-sky, infrared-selected sample built from WISE data, containing over 500 million galaxies. Using unWISE galaxy-galaxy auto-correlation and Planck CMB lensing-galaxy cross-correlation measurements down to 10 arcmin angular scales, we constrain the halo occupation distribution (HOD), a model describing how central and satellite galaxies are distributed within dark matter halos, for three unWISE} galaxy samples at mean redshifts $\bar{z} \approx 0.6$, $1.1$, and $1.5$. We constrain the characteristic minimum halo mass to host a central galaxy, $M_\mathrm{min}^\mathrm{HOD} = 1.83^{+0.41}_{-1.63} \times 10^{12} M_\odot/h$, $5.22^{+0.34}_{-4.80} \times 10^{12} M_\odot/h$, $6.60 ^{+0.30}_{-1.11} \times 10^{13} M_\odot/h$ for the unWISE samples at $\bar{z}\approx 0.6$, $1.1$, and $1.5$, respectively. We find that all three samples are dominated by central galaxies, rather than satellites. Using our constrained HOD models, we infer the effective linear galaxy bias for each unWISE sample, and find that it does not evolve as steeply with redshift as found in previous perturbation-theory-based analyses of these galaxies. We discuss possible sources of systematic uncertainty in our results, the most significant of which is the uncertainty on the galaxy redshift distribution. Our HOD constraints provide a detailed, quantitative understanding of how the unWISE galaxies populate the underlying dark matter halo distribution. These constraints will have a direct impact on future studies employing the unWISE galaxies as a cosmological and astrophysical probe, including measurements of ionized gas thermodynamics and dark matter profiles via Sunyaev-Zel'dovich and lensing cross-correlations.

Effectively Investigating Dark Matter Microphysics With Strong Gravitational Lensing Anisotropies

AbstractOver the last two decades, strong gravitational lensing has emerged as a potential method for studying the nature and distribution of dark matter on sub-galactic scales. In addition to the main lens substructure, line-of-sight dark matter haloes contribute greatly to the subtle perturbations of lensed images. Line-of-sight haloes, unlike dark matter subhaloes, imprint distinct anisotropic and quadrupole signatures in the maps that depict the divergence and curl of the effective deflection field, respectively, giving rise to quadrupole moments of the image-plane averaged two-point correlation function of these maps. In terms of central density evolution and dark matter halo distribution, the shapes and amplitudes of the two-point function multipoles alter dramatically in the presence of warm dark matter and self-interacting dark matter. This method, in conjunction with upcoming large-scale surveys, provides the prospect of improving the constraints on dark matter at a critical time in strong gravitational lensing research.

Sub-GeV dark matter at accelerators

Motivations for studying light MeV-to-GeV mass dark matter scenarios and opportunities for probing these models with high intensity fixed target experiments are reviewed. Attention is focussed on the lower O(MeV) end of the thermal relic mass window, as relevant for high-intensity accelerators probing nuclear structure, and the complexities of models in this mass range due to cosmological constraints such as the bound on N eff. Existing limits, including those from direct detection electron-scattering experiments sensitive to a sub-leading but more energetic solar reflected component of the dark matter halo distribution, are also reviewed.

Insights into the origin of halo mass profiles from machine learning

ABSTRACT The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable machine-learning framework to provide physical insights into the origin of the spherically-averaged mass profile of dark matter haloes. We train a gradient-boosted-trees algorithm to predict the final mass profiles of cluster-sized haloes, and measure the importance of the different inputs provided to the algorithm. We find two primary scales in the initial conditions (ICs) that impact the final mass profile: the density at approximately the scale of the haloes’ Lagrangian patch RL ($R\sim 0.7\, R_L$) and that in the large-scale environment (R ∼ 1.7 RL). The model also identifies three primary time-scales in the halo assembly history that affect the final profile: (i) the formation time of the virialized, collapsed material inside the halo, (ii) the dynamical time, which captures the dynamically unrelaxed, infalling component of the halo over its first orbit, (iii) a third, most recent time-scale, which captures the impact on the outer profile of recent massive merger events. While the inner profile retains memory of the ICs, this information alone is insufficient to yield accurate predictions for the outer profile. As we add information about the haloes’ mass accretion history, we find a significant improvement in the predicted profiles at all radii. Our machine-learning framework provides novel insights into the role of the ICs and the mass assembly history in determining the final mass profile of cluster-sized haloes.

The cosmic web connection to the dark matter halo distribution through gravity

ABSTRACT This work investigates the connection between the cosmic web and the halo distribution through the gravitational potential at the field level. We combine three fields of research, cosmic web classification, perturbation theory expansions of the halo bias, and halo (galaxy) mock catalogue making methods. In particular, we use the invariants of the tidal field and the velocity shear tensor as generating functions to reproduce the halo number counts of a reference catalogue from full gravity calculations, populating the dark matter field on a mesh well into the non-linear regime ($3\, h^{-1}\, {\rm Mpc}$ scales). Our results show an unprecedented agreement with the reference power spectrum within 1 per cent up to $k=0.72\, h\, {\rm Mpc}^{-1}$. By analysing the three-point statistics on large scales (configurations of up to $k=0.2\, h\, {\rm Mpc}^{-1}$), we find evidence for non-local bias at the 4.8σ confidence level, being compatible with the reference catalogue. In particular, we find that a detailed description of tidal anisotropic clustering on large scales is crucial to achieve this accuracy at the field level. These findings can be particularly important for the analysis of the next generation of galaxy surveys in mock galaxy production.

Phenomenological distribution of dark matter halos of galaxy comprising of stellar streams

Using the potential associated with NFW mass profile (Navarro et al. in Mon. Not. R. Astron. Soc. 402:21, 2010) enclosed at scale radius $r_{s}$ , we calculate the partition function of the clustering of dark halos comprising of stellar streams, which are the outcome of accretion of globular clusters or dwarf galaxies onto a massive host galaxy. The thermodynamics of the system of halos of stellar streams may provide crucial information on the properties of galaxy clustering. The distribution function is studied through statistical formalism. The study can give us an idea of the extended structure of galaxies and the contribution of halos to the thermodynamics and the mass content of the host galaxy. In an attempt to fit the theoretical orbital velocity to the galaxy rotation curve for the data available for galaxy $M33$ , we developed another potential that exactly leads to the best fit orbital velocity of the galaxy. Thus a phenomenological theory for the galaxy with a dark halo is established, with a new density profile.

The persistence of large scale structures. Part I. Primordial non-Gaussianity

We develop an analysis pipeline for characterizing the topology of large scale structure and extracting cosmological constraints based on persistent homology. Persistent homology is a technique from topological data analysis that quantifies the multiscale topology of a data set, in our context unifying the contributions of clusters, filament loops, and cosmic voids to cosmological constraints. We describe how this method captures the imprint of primordial local non-Gaussianity on the late-time distribution of dark matter halos, using a set of N-body simulations as a proxy for real data analysis. For our best single statistic, running the pipeline on several cubic volumes of size 40 (Gpc/h)3, we detect fNL loc=10 at 97.5% confidence on ∼ 85% of the volumes. Additionally wetest our ability to resolve degeneracies betweenthe topological signature of fNL loc and variation of σ8 and argue that correctly identifying nonzero fNL loc in this case is possible via an optimal template method. Our method relies on information living at \U0001d4aa(10) Mpc/h, a complementary scale with respect to commonly used methods such as the scale-dependent bias in the halo/galaxy power spectrum. Therefore, while still requiring a large volume, our method does not require sampling long-wavelength modes to constrain primordial non-Gaussianity. Moreover, our statistics are interpretable: we are able to reproduce previous results in certain limits and we make new predictions for unexplored observables, such as filament loops formed by dark matter halos in a simulation box.

Intensity mapping as a probe of axion dark matter

ABSTRACT We consider intensity mapping (IM) of neutral hydrogen (H i) in the redshift range 0 ≲ z ≲ 3 employing a halo model approach where H i is assumed to follow the distribution of dark matter (DM) haloes. If a portion of the DM is composed of ultralight axions, then the abundance of haloes is changed compared to cold DM below the axion Jeans mass. With fixed total H i density, $\Omega _{\rm H\, \rm {\small I}}$, assumed to reside entirely in haloes, this effect introduces a scale-independent increase in the H i power spectrum on scales above the axion Jeans scale, which our model predicts consistent with N-body simulations. Lighter axions introduce a scale-dependent feature even on linear scales due to its suppression of the matter power spectrum near the Jeans scale. We use the Fisher matrix formalism to forecast the ability of future H i surveys to constrain the axion fraction of DM and marginalize over astrophysical and model uncertainties. We find that a HIRAX-like survey is a very reliable IM survey configuration, being affected minimally by uncertainties due to non-linear scales, while the SKA1MID configuration is the most constraining as it is sensitive to non-linear scales. Including non-linear scales and combining a SKA1MID-like IM survey with the Simons Observatory CMB, the benchmark ‘fuzzy DM’ model with ma = 10−22 eV can be constrained at few per cent. This is almost an order of magnitude improvement over current limits from the Ly α forest. For lighter ULAs, this limit improves below 1 per cent, and allows the possibility to test the connection between axion models and the grand unification scale across a wide range of masses.

Neural physical engines for inferring the halo mass distribution function

ABSTRACT An ambitious goal in cosmology is to forward model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be done is necessarily limited. Consequently, one needs a mapping between the density of dark matter averaged over ∼Mpc scales and the distribution of dark matter haloes (used as a proxy for galaxies) in the same region. Here, we demonstrate a method for determining the halo mass distribution function by learning the tracer bias between density fields and halo catalogues using a neural bias model. The method is based on the Bayesian analysis of simple, physically motivated, neural network-like architectures, which we denote as neural physical engines, and neural density estimation. As a result, we are able to sample the initial phases of the dark matter density field while inferring the parameters describing the halo mass distribution function, providing a fully Bayesian interpretation of both the initial dark matter density distribution and the neural bias model. We successfully run an upgraded borg (Bayesian Origin Reconstruction from Galaxies) inference using our new likelihood and neural bias model with halo catalogues derived from full N-body simulations. In preliminary results, we notice there could potentially be orders of magnitude improvement in modelling compared to classical biasing techniques.

Dark Matter Haloes and Subhaloes

The development of methods and algorithms to solve the N-body problem for classical, collisionless, non-relativistic particles has made it possible to follow the growth and evolution of cosmic dark matter structures over most of the universe’s history. In the best-studied case—the cold dark matter or CDM model—the dark matter is assumed to consist of elementary particles that had negligible thermal velocities at early times. Progress over the past three decades has led to a nearly complete description of the assembly, structure, and spatial distribution of dark matter haloes, and their substructure in this model, over almost the entire mass range of astronomical objects. On scales of galaxies and above, predictions from this standard CDM model have been shown to provide a remarkably good match to a wide variety of astronomical data over a large range of epochs, from the temperature structure of the cosmic background radiation to the large-scale distribution of galaxies. The frontier in this field has shifted to the relatively unexplored subgalactic scales, the domain of the central regions of massive haloes, and that of low-mass haloes and subhaloes, where potentially fundamental questions remain. Answering them may require: (i) the effect of known but uncertain baryonic processes (involving gas and stars), and/or (ii) alternative models with new dark matter physics. Here we present a review of the field, focusing on our current understanding of dark matter structure from N-body simulations and on the challenges ahead.

Painting halos from cosmic density fields of dark matter with physically motivated neural networks

We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions. This map is provided via a physically motivated network with which we can learn the non-trivial local relation between dark matter density field and halo distributions without relying on a physical model. Unlike other generative or regressive models, a well motivated prior and simple physical principles allow us to train the mapping network quickly and with relatively little data. In learning to paint halo distributions from computationally cheap, analytical and non-linear density fields, we bypass the need for full particle mesh simulations and halo finding algorithms. Furthermore, by design, our halo painting network needs only local patches of dark matter density to predict the halos, and as such, it can predict the 3D halo distribution for any arbitrary simulation box size. Our neural network can be trained using small simulations and used to predict large halo distributions, as long as the resolutions are equivalent. We evaluate our model's ability to generate 3D halo count distributions which reproduce, to a high degree, summary statistics such as the power spectrum and bispectrum, of the input or reference realizations.

Dark matter haloes in the multicomponent model – I. Substructure

Multicomponent dark matter with self-interactions, which allows for inter-conversions of species into one another, is a promising paradigm that is known to successfully and simultaneously resolve major problems of the conventional $\Lambda$CDM cosmology at galactic and sub-galactic scales. In this paper, we present $N$-body simulations of the simplest two-component (2cDM) model aimed at studying the distribution of dark matter halos with masses $M\lesssim10^{12}M_\odot$. In particular, we investigate how the maximum circular velocity function of the halos is affected by the velocity dependence of the self-interaction cross-sections, $\sigma(v)\propto v^a$, and compare them with available observational data. The results demonstrate that the 2cDM paradigm with the range of self-interaction cross-section per particle mass (evaluated at $v=100$ km s$^{-1}$) of $0.01\lesssim \sigma_0/m\lesssim 1 $ cm$^2$g$^{-1}$ and the mass degeneracy $\Delta m/m\sim 10^{-7}-10^{-8}$ is robustly resolving the substructure and too-big-to-fail problems by suppressing the substructure having small maximum circular velocities, $V_{\rm max}\lesssim100$ km s$^{-1}$. We also discuss the disagreement between the radial distribution of dwarfs in a host halo observed in the Local Group and simulated with CDM. This can be considered as one more small-scale problem of CDM. We demonstrate that such a disagreement is alleviated in 2cDM. Finally, the computed matter power-spectra of the 2cDM structure indicate the model's consistency with the existing Ly-$\alpha$ forest constraints.

Properties of the Intracluster Medium Assuming an Einasto Dark Matter Profile

I investigate an analytical model of galaxy clusters based on the assumptions that the intracluster medium plasma is polytropic and is in hydrostatic equilibrium. The Einasto profile is adopted as a model for the spatial-density distribution of dark matter halos. This model has sufficient degrees of freedom to simultaneously fit X-ray surface brightness and temperature profiles, with five parameters to describe the global cluster properties and three additional parameters to describe the cluster’s cool-core feature. The model is tested with Chandra X-ray data for seven galaxy clusters, including three polytropic clusters and four cool-core clusters. It is found that the model accurately reproduces the X-ray data over most of the radial range. For all galaxy clusters, the data allows one to show that the model is essentially as good as that of Vikhlinin et al. and Bulbul et al., as inferred by the reduced χ 2.