We present a comprehensive study of the distribution of matter around different populations of filaments, using the IllustrisTNG simulation at z=0. We compute the dark matter (DM), gas, and stellar radial density profiles of filaments, and we charact
erise the distribution of the baryon fraction in these structures. We find that baryons exactly follow the underlying DM distribution only down to r~7 Mpc to the filament spines. At shorter distances (r<7 Mpc) the baryon fraction profile of filaments departs from the cosmic value Omega_b/Omega_m. While in the r~0.7 - 7 Mpc radial domain this departure is due to the radial accretion of WHIM gas towards the filament cores (creating an excess of baryons with respect to the cosmic fraction), the cores of filaments (r<0.7 Mpc) show instead a clear baryon depletion, quantified by a depletion factor of Y_b = 0.63-0.68. The analysis of the efficiency of AGN feedback events in filaments reveals that they are potentially powerful enough to eject gas outside of the gravitational potential wells of filaments. We show that the large-scale environment (i.e. denser vs less-dense, hotter vs colder regions) has a non-negligible effect on the absolute values of the DM, gas, and stellar densities around filaments. Nevertheless, the relative distribution of baryons with respect to the underlying DM density field is found to be independent from the filament population. Finally, we provide scaling relations between gas density, temperature, and pressure for the different populations of cosmic filaments. We compare these relations to those pertaining to clusters of galaxies, and find that these cosmic structures occupy separate regions of the density-temperature and density-pressure planes.
We conducted an X-ray analysis of one of the two Planck-detected triplet-cluster systems, PLCK G334.8-38.0, with a $sim100$~ks deep XMM-Newton data. We find that the system has a redshift of $z=0.37pm{0.01}$ but the precision of the X-ray spectroscop
y for two members is too low to rule out a projected triplet system, demanding optical spectroscopy for further investigation. In projection, the system looks almost like an equilateral triangle with an edge length of $sim2.0,mathrm{Mpc}$, but masses are very unevenly distributed ($M_{500} sim [2.5,0.7,0.3] times 10^{14},mathrm{M_{odot}}$ from bright to faint). The brightest member appears to be a relaxed cool-core cluster and is more than twice as massive as both other members combined. The second brightest member appears to be a disturbed non-cool-core cluster and the third member was too faint to make any classification. None of the clusters have an overlapping $R_{500}$ region and no signs of cluster interaction were found; however, the XMM-Newton data alone are probably not sensitive enough to detect such signs, and a joint analysis of X-ray and the thermal Sunyaev-Zeldovich effect (tSZ) is needed for further investigation, which may also reveal the presence of the warm-hot intergalactic medium (WHIM) within the system. The comparison with the other Planck-detected triplet-cluster-system (PLCK G214.6+36.9) shows that they have rather different configurations, suggesting rather different merger scenarios, under the assumption that they are both not simply projected triplet systems.
A regularized version of Mixture Models is proposed to learn a principal graph from a distribution of $D$-dimensional data points. In the particular case of manifold learning for ridge detection, we assume that the underlying manifold can be modeled
as a graph structure acting like a topological prior for the Gaussian clusters turning the problem into a maximum a posteriori estimation. Parameters of the model are iteratively estimated through an Expectation-Maximization procedure making the learning of the structure computationally efficient with guaranteed convergence for any graph prior in a polynomial time. We also embed in the formalism a natural way to make the algorithm robust to outliers of the pattern and heteroscedasticity of the manifold sampling coherently with the graph structure. The method uses a graph prior given by the minimum spanning tree that we extend using random sub-samplings of the dataset to take into account cycles that can be observed in the spatial distribution.
Velocity field provides a complementary avenue to constrain cosmological information, either through the peculiar velocity surveys or the kinetic Sunyaev Zeldovich effect. One of the commonly used statistics is the mean radial pairwise velocity. Here
, we consider the three-point mean relative velocity, i.e. the mean relative velocities between pairs in a triplet. Using halo catalogs from the Quijote suite of N-body simulations, we first showcase how the analytical prediction for the mean relative velocities between pairs in a triplet achieve better than 4-5% accuracy using standard perturbation theory at leading order for triangular configurations with a minimum separation of $r geq 50 h^{-1}$Mpc. Furthermore, we present the three-point relative velocity as a novel probe of neutrino mass estimation. We explore the full cosmological information content of the halo mean pairwise velocities, and the mean relative velocities between halo pairs in a triplet. We undertake this through the Fisher-matrix formalism using 22,000 simulations from the Quijote suite, and considering all triangular configurations with a minimum and a maximum separation of $20 h^{-1}$Mpc and $120 h^{-1}$Mpc, respectively. We find that the mean relative velocities in a triplet allows a 1$sigma$ neutrino mass ($M_ u$) constraint of 0.065 eV, that is roughly 13 times better than the mean pairwise velocity constraint (0.877 eV). This information gain is not limited only to neutrino mass, but extends to other cosmological parameters: $Omega_{mathrm{m}}$, $Omega_{mathrm{b}}$, $h$, $n_{mathrm{s}}$ and $sigma_{8}$ achieving a gain of 8.9, 11.8, 15.5, 20.9 and 10.9 times respectively. These results illustrate the possibility of exploiting the mean three-point relative velocities for constraining the cosmological parameters accurately from future cosmic microwave background experiments and peculiar velocity surveys.
Recently a type of neural networks called Generative Adversarial Networks (GANs) has been proposed as a solution for fast generation of simulation-like datasets, in an attempt to bypass heavy computations and expensive cosmological simulations to run
in terms of time and computing power. In the present work, we build and train a GAN to look further into the strengths and limitations of such an approach. We then propose a novel method in which we make use of a trained GAN to construct a simple autoencoder (AE) as a first step towards building a predictive model. Both the GAN and AE are trained on images issued from two types of N-body simulations, namely 2D and 3D simulations. We find that the GAN successfully generates new images that are statistically consistent with the images it was trained on. We then show that the AE manages to efficiently extract information from simulation images, satisfyingly inferring the latent encoding of the GAN to generate an image with similar large scale structures.
We present the study of gas phases around cosmic-web filaments detected in the TNG300-1 hydro-dynamical simulation at redshift z=0. We separate the gas in five different phases according to temperature and density. We show that filaments are essentia
lly dominated by gas in the warm-hot intergalactic medium (WHIM), which accounts for more than 80% of the baryon budget at $r sim 1$ Mpc. Apart from WHIM gas, cores of filaments ($r<1$ Mpc) also host large contributions other hotter and denser gas phases, whose fractions depend on the filament population. By building temperature and pressure profiles, we find that gas in filaments is isothermal up to $r sim 1.5$ Mpc, with average temperatures of T_core = $4-13 times 10^5$ K, depending on the large scale environment. Pressure at cores of filaments is on average P_core = $4-12 times 10^{-7}$ keV/cm^3, which is ~1000 times lower than pressure measured in observed clusters. We also estimate that the observed Sunyaev-Zeldovich (SZ) signal from cores of filaments should range between $0.5 < y < 4.1 times 10^{-8}$, and these results are compared with recent observations. Our findings show that the state of the gas in filaments depend on the presence of haloes, and on the large scale environment.
We explore the impact of baryonic effects (namely stellar and AGN feedback) on the moments of pairwise velocity using the Illustris-TNG, EAGLE, cosmo-OWLS, and BAHAMAS suites of cosmological hydrodynamical simulations. The assumption that the mean pa
irwise velocity of the gas component follows that of the dark matter is studied here at small separations, and we find that even at pair separations of 10-20 $h^{-1}mathrm{Mpc}$ there is a 4-5% velocity bias. At smaller separations, it gets larger with strength varying depending on the subgrid prescription. By isolating different physical processes, our findings suggest that the large scale velocity bias is mainly driven by stellar rather than AGN feedback. If unaccounted for, this velocity offset could possibly bias cosmological constraints from the kinetic Sunyaev-Zeldovich effect in future cosmic microwave background (CMB) surveys. Furthermore, we examine how the first and the second moment of the pairwise velocity are affected by both the baryonic and the neutrino free-streaming effects for both the matter and gas components. For both moments, we were able to disentangle the effects of baryonic processes from those of massive neutrinos; and below pair separations of 20 $h^{-1}mathrm{Mpc}$, we find that these moments of the pairwise velocity decrease with increasing neutrino mass. Our work thus paves a way in which the pairwise velocity statistics can be utilised to constrain the summed mass of neutrinos from future CMB surveys and peculiar velocity surveys.
We report the direct detection of the kinetic Sunyaev-Zeldovich (kSZ) effect in galaxy clusters with a 3.5 sigma significance level. The measurement was performed by stacking the Planck map at 217 GHz at the positions of galaxy clusters from the Wen-
Han-Liu (WHL) catalog. To avoid the cancelation of positive and negative kSZ signals, we used the large-scale distribution of the Sloan Digital Sky Survey (SDSS) galaxies to estimate the peculiar velocities of the galaxy clusters along the line of sight and incorporated the sign in the velocity-weighted stacking of the kSZ signals. Using this technique, we were able to measure the kSZ signal around galaxy clusters beyond 3R500. Assuming a standard beta-model, we also found that the gas fraction within R500 is fgas,500 = 0.12 +- 0.04 for the clusters with the mass of M500 ~ 1e14 Msun/h. We compared this result to predictions from the Magneticum cosmological hydrodynamic simulations as well as other kSZ and X-ray measurements, most of which show a lower gas fraction than the universal baryon fraction for the same mass of clusters. Our value is statistically consistent with results from the measurements and simulations and also with the universal value within our measurement uncertainty.
Galaxy clusters are a recent cosmological probe. The precision and accuracy of the cosmological parameters inferred from these objects are affected by the knowledge of cluster physics, entering the analysis through the mass-observable scaling relatio
ns, and the theoretical description of their mass and redshift distribution, modelled by the mass function. In this work, we forecast the impact of different modelling of these ingredients for clusters detected by future optical and near-IR surveys. We consider the standard cosmological scenario and the case with a time-dependent equation of state for dark energy. We analyse the effect of increasing accuracy on the scaling relation calibration, finding improved constraints on the cosmological parameters. This higher accuracy exposes the impact of the mass function evaluation, which is a subdominant source of systematics for current data. We compare two different evaluations for the mass function. In both cosmological scenarios, the use of different mass functions leads to biases in the parameter constraints. For the $Lambda$CDM model, we find a $1.6 , sigma$ shift in the $(Omega_m,sigma_8)$ parameter plane and a discrepancy of $sim 7 , sigma$ for the redshift evolution of the scatter of the scaling relations. For the scenario with a time-evolving dark energy equation of state, the assumption of different mass functions results in a $sim 8 , sigma$ tension in the $w_0$ parameter. These results show the impact, and the necessity for a precise modelling, of the interplay between the redshift evolution of the mass function and of the scaling relations in the cosmological analysis of galaxy clusters.
Detecting the large-scale structure of the Universe based on the galaxy distribution and characterising its components is of fundamental importance in astrophysics but is also a difficult task to achieve. Wide-area spectroscopic redshift surveys are
required to accurately measure galaxy positions in space that also need to cover large areas of the sky. It is also difficult to create algorithms that can extract cosmic web structures (e.g. filaments). Moreover, these detections will be affected by systematic uncertainties that stem from the characteristics of the survey used (e.g. its completeness and coverage) and from the unique properties of the specific method adopted to detect the cosmic web (i.e. the assumptions it relies on and the free parameters it may employ). For these reasons, the creation of new catalogues of cosmic web features on wide sky areas is important, as this allows users to have at their disposal a well-understood sample of structures whose systematic uncertainties have been thoroughly investigated. In this paper we present the filament catalogues created using the discrete persistent structure extractor (DisPerSE) tool in the Sloan Digital Sky Survey (SDSS), and we fully characterise them in terms of their dependence on the choice of parameters pertaining to the algorithm, and with respect to several systematic issues that may arise in the skeleton as a result of the properties of the galaxy distribution (such as Finger-of-God redshift distortions and defects of the density field that are due to the boundaries of the survey).
