Void Catalog Research Articles

The cosmic microwave background lensing imprint of cosmic voids detected in the WISE-Pan-STARRS luminous red galaxy catalog

The cross-correlation of cosmic voids with the lensing convergence (kappa ) map of cosmic microwave background (CMB) fluctuations offers a powerful tool with which to refine our understanding of the dark sector in the consensus cosmological model. Our principal aim is to compare the lensing signature of our galaxy dataset with simulations based on the concordance model and to characterize the results with an $A_ kappa $ consistency parameter normalized to unity. In particular, our measurements contribute to the understanding of the “lensing-is-low” tension of the Lambda CDM model. In this analysis, we selected luminous red galaxies (LRGs) from the WISE- Pan-STARRS dataset, enabling an extended cross-correlation measurement using a 14,200 deg$^2$ sky area, which offers a more precise measurement than previous studies. We created 2D and 3D void catalogs to cross-correlate their locations with the Planck CMB lensing map and studied their average imprint signal using a stacking methodology. Applying the same procedure, we also generated a mock galaxy catalog from the WebSky simulation to serve as a basis for comparison. The 2D void analysis reveals a good agreement with the standard cosmological model, with $A_ kappa 0.08$ amplitude; that is, $S/N=13.3$, showing a higher signal-to-noise than previous studies using voids detected in the Dark Energy Survey (DES) dataset. The 3D void analysis exhibited a lower signal-to-noise ratio and demonstrated worse agreement with our mock catalog than the 2D voids. These deviations might be attributed to limitations in the mock catalog, such as imperfections in the LRG selection, as well as a potential asymmetry between the northern and southern patches of the Pan-STARRS dataset in terms of data quality. Overall, we present a significant detection of a CMB lensing signal associated with cosmic voids, largely consistent with the concordance model. Future analyses using even larger datasets also hold great promise of further sharpening these results, given their complementary nature to large-scale structure analyses.

Influence of cosmic voids on the propagation of TeV gamma-rays and the puzzle of GRB 221009A

ABSTRACT The recent detection of gamma-ray burst GRB 221009A has attracted attention due to its record brightness and first-ever detection of $\gtrsim 10$ TeV gamma-rays from a GRB. Despite being the second-nearest GRB ever detected, at a redshift of $z=0.151$, the distance is large enough for severe attenuation of gamma-ray flux at these energies due to $\gamma \gamma \rightarrow e^\pm$ pair production with the extragalactic background light (EBL). Here, we investigate whether the presence of cosmic voids along the line of sight can significantly impact the detectability of very high energy (VHE, $\gt $100 GeV) gamma-rays from distant sources. Notably, we find that the gamma–gamma opacity for VHE gamma-rays can be reduced by approximately 10 per cent and up to 30 per cent at around 13 TeV, the highest-energy photon detected from GRB 221009A, for intervening cosmic voids along the line of sight with a combined radius of 110 Mpc, typically found from void catalogues, and 250 Mpc, respectively. This reduction is substantially higher for TeV photons compared to GeV photons, attributable to the broader target photon spectrum that TeV photons interact with. This finding implies that VHE photons are more susceptible to variations in the EBL spectrum, especially in regions dominated by cosmic voids. Our study sheds light on the detection of $\gtrsim 10$ TeV gamma-rays from GRB 221009A in particular, and on the detection of extragalactic VHE sources in general.

A Comparison of Void-finding Algorithms Using Crossing Numbers

We study how well void-finding algorithms identify cosmic void regions and whether we can quantitatively and qualitatively compare the voids they find with dynamical information from the underlying matter distribution. Using the ORIGAMI algorithm to determine the number of dimensions along which dark matter particles have undergone shell crossing (crossing number) in N-body simulations from the AbacusSummit simulation suite, we identify dark matter particles that have undergone no shell crossing as belonging to voids. We then find voids in the corresponding halo distribution using two different void-finding algorithms: VoidFinder and V2, a ZOBOV-based algorithm. The resulting void catalogs are compared to the distribution of dark matter particles to examine how their crossing numbers depend on void proximity. While both algorithms’ voids have a similar distribution of crossing numbers near their centers, we find that beyond 0.25 times the effective void radius, voids found by VoidFinder exhibit a stronger preference for particles with low crossing numbers than those found by V2. We examine two possible methods of mitigating this difference in efficacy between the algorithms. While we are able to partially mitigate the ineffectiveness of V2 by using the distance from the void edge as a measure of centrality, we conclude that VoidFinder more reliably identifies dynamically distinct regions of low crossing number.

Machine-learning Cosmology from Void Properties

Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the Universe. We show that machine-learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES data set, where every catalog contains an average of 11,000 voids from a volume of . We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train (1) fully connected neural networks on histograms from individual void properties and (2) deep sets from void catalogs to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of Ω m , σ 8, and n s with mean relative errors of 10%, 4%, and 3%, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.

The CAMELS Project: Public Data Release

The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lyα spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io.

Updated Void Catalogs of the SDSS DR7 Main Sample

We produce several public void catalogs using a volume-limited subsample of the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Using new implementations of three different void-finding algorithms, VoidFinder and two ZOBOV-based algorithms (VIDE and REVOLVER), we identify 1163, 531, and 518 cosmic voids with radii >10 h −1 Mpc, respectively, out to a redshift of z = 0.114 assuming a Planck 2018 cosmology, and 1184, 535, and 519 cosmic voids assuming a WMAP5 cosmology. We compute effective radii and centers for all voids and find none with an effective radius >54 h −1 Mpc. The median void effective radius is 15–19 h −1 Mpc for all three algorithms. We extract and discuss several properties of the void populations, including radial density profiles, the volume fraction of the catalog contained within voids, and the fraction of galaxies contained within voids. Using 64 mock galaxy catalogs created from the Horizon Run 4 N-body simulation, we compare simulated and observed void properties and find good agreement between the SDSS DR7 and mock catalog results.

The GIGANTES Data Set: Precision Cosmology from Voids in the Machine-learning Era

We present GIGANTES, the most extensive and realistic void catalog suite ever released—containing over 1 billion cosmic voids covering a volume larger than the observable universe, more than 20 TB of data, and created by running the void finder VIDE on QUIJOTE’s halo simulations. The GIGANTES suite, spanning thousands of cosmological models, opens up the study of voids, answering compelling questions: Do voids carry unique cosmological information? How is this information correlated with galaxy information? Leveraging the large number of voids in the GIGANTES suite, our Fisher constraints demonstrate voids contain additional information, critically tightening constraints on cosmological parameters. We use traditional void summary statistics (void size function, void density profile) and the void autocorrelation function, which independently yields an error of 0.13 eV on ∑ m ν for a 1 h −3 Gpc3 simulation, without cosmic microwave background priors. Combining halos and voids we forecast an error of 0.09 eV from the same volume, representing a gain of 60% compared to halos alone. Extrapolating to next generation multi-Gpc3 surveys such as the Dark Energy Spectroscopic Instrument, Euclid, the Spectro-Photometer for the History of the Universe and Ices Explorer, and the Roman Space Telescope, we expect voids should yield an independent determination of neutrino mass. Crucially, GIGANTES is the first void catalog suite expressly built for intensive machine-learning exploration. We illustrate this by training a neural network to perform likelihood-free inference on the void size function, giving a ∼20% constraint on Ωm. Cosmology problems provide an impetus to develop novel deep-learning techniques. With GIGANTES, machine learning gains an impressive data set, offering unique problems that will stimulate new techniques.

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: growth rate of structure measurement from cosmic voids

ABSTRACTWe present a void clustering analysis in configuration-space using the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 samples. These samples consist of Luminous Red Galaxies (LRGs) combined with the high-redshift tail of the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS) DR12 CMASS galaxies (called as LRG+CMASS sample), Emission Line Galaxies (ELGs), and quasars (QSOs). We build void catalogues from the three eBOSS DR16 samples using a ZOBOV-based algorithm, providing 2814 voids, 1801 voids, and 4347 voids in the LRG+CMASS, ELG, and QSO samples, respectively, spanning the redshift range 0.6 < z < 2.2. We measure the redshift space distortions around voids using the anisotropic void-galaxy cross-correlation function and we extract the distortion parameter β. We test the methodology on realistic simulations before applying it to the data, and we investigate all our systematic errors on these mocks. We find βLRG(z = 0.74) = 0.415 ± 0.087, βELG(z = 0.85) = 0.665 ± 0.125 and βQSO(z = 1.48) = 0.313 ± 0.134, for the LRG+CMASS, ELG, and QSO sample, respectively. The quoted errors include systematic and statistical contributions. In order to convert our measurements in terms of the growth rate fσ8, we use consensus values of linear bias from the eBOSS DR16 companion papers, resulting in the following constraints: fσ8(z = 0.74) = 0.50 ± 0.11, fσ8(z = 0.85) = 0.52 ± 0.10, and fσ8(z = 1.48) = 0.30 ± 0.13. Our measurements are consistent with other measurements from eBOSS DR16 using conventional clustering techniques.

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: cosmological implications from multitracer BAO analysis with galaxies and voids

ABSTRACT We construct cosmic void catalogues with the dive void finder upon SDSS BOSS DR12 and eBOSS DR16 galaxy samples with BAO reconstruction, and perform a joint BAO analysis using different types of galaxies and the corresponding voids. The BAO peak is evident for the galaxy–galaxy, galaxy–void, and void–void correlation functions of all data sets, including the ones cross-correlating LRG and ELG samples. Two multitracer BAO fitting schemes are tested, one combining the galaxy and void correlation functions with a weight applied to voids, and the other using a single BAO dilation parameter for all clustering measurements. Both methods produce consistent results with mock catalogues, and on average ∼10 per cent improvements of the BAO statistical uncertainties are observed for all samples, compared to the results from galaxies alone. By combining the clustering of galaxies and voids, the uncertainties of BAO measurements from the SDSS data are reduced by 5–15 per cent, yielding 0.9 per cent, 0.8 per cent, 1.1 per cent, 2.3 per cent, and 2.9 per cent constraints on the distance $D_{_{\rm V}}(z)$, at effective redshifts 0.38, 0.51, 0.70, 0.77, and 0.85, respectively. When combined with BAO measurements from SDSS MGS, QSO, and Lyα samples, as well as the BBN results, we obtain $H_0 = 67.58 \pm 0.91\, {\rm km}\, {\rm s}^{-1}\, {\rm Mpc}^{-1}$, Ωm = 0.290 ± 0.015, and $\Omega _\Lambda h^2 = 0.3241 \pm 0.0079$ in the flat-ΛCDM framework, where the 1σ uncertainties are around 6 per cent, 6 per cent, and 17 per cent smaller respectively, compared to constraints from the corresponding anisotropic BAO measurements without voids and LRG–ELG cross-correlations.

Catalogues of voids as antihaloes in the local Universe

ABSTRACT A recently proposed algorithm identifies voids in simulations as the regions associated with haloes when the initial overdensity field is negated. We apply this method to the real Universe by running a suite of constrained simulations of the 2M++ volume with initial conditions inferred by the BORG algorithm, along with the corresponding inverted set. Our 101 inverted and uninverted simulations, spanning the BORG posterior, each identify ∼150 000 ‘voids as antihaloes’ with mass exceeding 4.38 × 1011 M⊙ (100 particles) at z = 0 in a full-sky sphere of radius 155 Mpc h−1 around the Milky Way. We calculate the size function, volume filling fraction, ellipticity, central density, specific angular momentum, clustering, and stacked density profile of the voids, and cross-correlate them with those produced by VIDE on the same simulations. We make our antihalo and VIDE catalogues publicly available.

Void Galaxy Distribution: A Challenge for ΛCDM

Abstract We extract void catalogs from the Sloan Digital Sky Survey Data Release 16 (SDSS DR16) survey and also from the Millennium simulation. We focus our comparison on distribution of galaxies brighter than M r < −18 inside voids and study the mean separation of void galaxies, distance from the void center, and the radial density profile. We find that mean separation of void galaxies depends on void size, as bigger voids have lower mean separation in both samples. However, void galaxies in the observation sample seem to have generally larger mean–distance than simulated ones at any given void size. In addition, observed void galaxies tend to reside closer to the void center than those in the simulation. This discrepancy is also shown in the density profile of voids. Regardless of the void size, the central densities of real void profiles are higher than the ones in the predicted simulated catalog.

Environmental dependence of X-ray and optical properties of galaxy clusters

ABSTRACT Galaxy clusters are widely used to constrain cosmological parameters through their properties, such as masses, luminosity, and temperature distributions. One should take into account all kind of biases that could affect these analyses in order to obtain reliable constraints. In this work, we study the difference in the properties of clusters residing in different large-scale environments, defined by their position within or outside of voids, and the density of their surrounding space. We use both observational and simulation cluster and void catalogues, i.e. XMM Cluster Survey (XCS) and redMaPPer clusters, Baryon Oscillation Spectroscopic Survey (BOSS) voids, and Magneticum simulations. We devise two different environmental proxies for the clusters and study their redshift, richness, mass, X-ray luminosity, and temperature distributions, as well as some properties of their galaxy populations. We use the Kolmogorov–Smirnov two-sample test to discover that richer and more massive clusters are more prevalent in overdense regions and outside of voids. We also find that clusters of matched richness and mass in overdense regions and outside voids tend to have higher X-ray luminosities and temperatures. These differences could have important implications for precision cosmology with clusters of galaxies, since cluster mass calibrations can vary with environment.

How Do Galaxy Properties Affect Void Statistics?

Abstract Using mapping from dark matter halos to galaxy properties based on hydrodynamical simulations, we explore the impact of galaxy properties on the void size function and the void–galaxy correlation function. We replicate the properties of galaxies from Illustris on MassiveNus halos, to perform both luminosity and star formation rate cuts on MassiveNus halos. We compare the impact of such cuts on void properties with respect to cuts on halo mass (as usually performed on halo catalogs driven from N-body simulations). We find that void catalogs built from luminosity-selected galaxies and halos are consistent within errors, while void catalogs built from star formation rate-selected galaxies differ from void catalogs built on halos. We investigate the reason for this difference. Our work suggests that voids built on galaxy catalogs (selected through luminosity cuts) can be reliably studied using halos in dark matter simulations.

The Quijote Simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane. At a single redshift the simulations contain more than 8.5 trillions of particles over a combined volume of 44,100 $(h^{-1}{\rm Gpc})^3$; each simulation follow the evolution of $256^3$, $512^3$ or $1024^3$ particles in a box of $1~h^{-1}{\rm Gpc}$ length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: 1) to quantify the information content on cosmological observables, and 2) to provide enough data to train machine learning algorithms. In this paper we describe the simulations and show a few of their applications. We also release the Petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts, halo and void catalogs, together with millions of summary statistics such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.

A tomographic map of the large-scale matter distribution using the eBOSS—Stripe 82 Lyα forest

The Lyman-α (hereafter Lyα) forest is a probe of large-scale matter density fluctuations at high redshift, z > 2.1. It consists of H I absorption spectra along individual lines-of-sight. If the line-of-sight density is large enough, 3D maps of H I absorption can be inferred by tomographic reconstruction. In this article, we investigate the Lyα forest available in the Stripe 82 field (220 deg2), based on the quasar spectra from SDSS Data Release DR16. The density of observed quasar spectra is 37 deg−2 with a mean pixel signal-to-noise ratio of two per angstrom. This study provides an intermediate case between the average SDSS density and that of the much denser but smaller CLAMATO survey. We derive a 3D map of large-scale matter fluctuations from these data, using a Wiener filter technique. The total volume of the map is 0.94 h−3 Gpc3. Its resolution is 13 h−1 Mpc, which is related to the mean transverse distance between nearest lines-of-sight. From this map, we provide a catalog of voids and protocluster candidates in the cosmic web. The map-making and void catalog are compared to simulated eBOSS Stripe 82 observations. A stack over quasar positions provides a visualization of the Lyα forest-quasar cross-correlation. This tomographic reconstruction constitutes the largest-volume high-redshift 3D map of matter fluctuations.

Constraints on the growth of structure around cosmic voids in eBOSS DR14

We present catalogues of cosmic voids identified in the distribution of Luminous Red Galaxies (LRGs) and Quasi Stellar Objects (QSOs) in the fourteenth data release (DR14) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). We perform a multivariate analysis to assess the level of contamination in these catalogues by spurious Poisson underdensities. We find that the LRG void catalogue is largely free from contamination but that the QSO catalogue may be heavily contaminated. We analyse the multipoles of the void-galaxy cross-correlation function in these catalogues to obtain constraints on the growth rate of structure around voids. We find a value of β(z=0.703)=0.58+0.33−0.28 for the LRG voids and β(z=1.53)=0.15+0.13−0.12 for the QSO voids.

On the relative bias of void tracers in the Dark Energy Survey

Abstract Luminous tracers of large-scale structure are not entirely representative of the distribution of mass in our Universe. As they arise from the highest peaks in the matter density field, the spatial distribution of luminous objects is biased towards those peaks. On large scales, where density fluctuations are mild, this bias simply amounts to a constant offset in the clustering amplitude of the tracer, known as linear bias. In this work we focus on the relative bias between galaxies and galaxy clusters that are located inside and in the vicinity of cosmic voids, extended regions of relatively low density in the large-scale structure of the Universe. With the help of mock data we verify that the relation between galaxy and cluster overdensity around voids remains linear. Hence, the void-centric density profiles of different tracers can be linked by a single multiplicative constant. This amounts to the same value as the relative linear bias between tracers for the largest voids in the sample. For voids of small sizes, which typically arise in higher density regions, this constant has a higher value, possibly showing an environmental dependence similar to that observed for the linear bias itself. We confirm our findings by analysing data obtained during the first year of observations by the Dark Energy Survey. As a side product, we present the first catalogue of three-dimensional voids extracted from a photometric survey with a controlled photo-z uncertainty. Our results will be relevant in forthcoming analyses that attempt to use voids as cosmological probes.

Cluster-void degeneracy breaking: Neutrino properties and dark energy

Future large-scale spectroscopic astronomical surveys, e.g. Euclid, will enable the compilation of vast new catalogues of clusters and voids in the galaxy distribution. By combining the constraining power of both cluster and void number counts, such surveys could place stringent simultaneous limits on the sum of neutrino masses $M_\nu$ and the dark energy equation of state $w(z) = w_0 + w_a z/(1+z)$. For minimal normal-hierarchy neutrino masses, we forecast that Euclid clusters + voids ideally could reach uncertainties $\sigma(M_\nu) \lesssim 15$ meV, $\sigma(w_0) \lesssim~0.02$, $\sigma(w_a) \lesssim 0.07$, independent of other data. Such precision is competitive with expectations for e.g. galaxy clustering and weak lensing in future cosmological surveys, and could reject an inverted neutrino mass hierarchy at $\gtrsim 99\%$ confidence.

Multivariate analysis of cosmic void characteristics

Abstract The aim of this study is to distinguish genuine cosmic voids, found in a galaxy catalog by the void finder ZOBOV–VIDE, from under-dense regions in a Poisson distribution of objects. For this purpose, we perform two multivariate analyses using the following physical void characteristics: volume, redshift, density contrast, minimum density, contrast significance and number of member galaxies of the void. The multivariate analyses are trained on a catalog of voids obtained from a random Poisson distribution of points, used as background, and a catalog of voids identified in a mock galaxy catalog, used as signal. The classifications are then applied to voids extracted from the Data Release 12 sample of Luminous Red Galaxies in the redshift range 0.45 ≤ z ≤ 0.7 from the Sloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey (SDSS BOSS DR12 CMASS). Our results show that the resulting void catalog is nearly free of contamination by Poisson noise. We also study the effect of tracer sparsity and bias on the classification efficiencies.

An accurate linear model for redshift space distortions in the void–galaxy correlation function

Redshift space distortions within voids provide a unique method to test for environmental dependence of the growth rate of structures in low density regions, where effects of modified gravity theories might be important. We derive a linear theory model for the redshift space void-galaxy correlation that is valid at all pair separations, including deep within the void, and use this to obtain expressions for the monopole $\xi^s_0$ and quadrupole $\xi^s_2$ contributions. Our derivation highlights terms that have previously been neglected but are important within the void interior. As a result our model differs from previous works and predicts new physical effects, including a change in the sign of the quadrupole term within the void radius. We show how the model can be generalised to include a velocity dispersion. We compare our model predictions to measurements of the correlation function using mock void and galaxy catalogues modelled after the BOSS CMASS galaxy sample using the Big MultiDark $N$-body simulation, and show that the linear model with dispersion provides an excellent fit to the data at all scales, $0\leq s\leq120\;h^{-1}$Mpc. While the RSD model matches simulations, the linear bias approximation does not hold within voids, and care is needed in fitting for the growth rate. We show that fits to the redshift space correlation recover the growth rate $f(z=0.52)$ to a precision of $2.7\%$ using the simulation volume of $(2.5\;h^{-1}\mathrm{Gpc})^3$.