It is generally assumed that scalar field dark matter halos would contain solitonic cores spherically symmetric ground state configurations at their centers. This is especially interesting in the case of ultralight dark matter (ULDM), where the solitons sizes are on the order of galaxies. In this work, we show that the paradigm of a spherically symmetric soliton embedded in the center of each halo is not the only plausible structure formation scenario when there are multiple, interacting scalar fields. In particular, sufficiently strong repulsive interspecies interactions make the fields separate, creating the possibility of scenarios where there are distinct solitons. In such models, the ground state configuration can fall into a number of different phases that depend on the fields' relative densities, masses, and interaction strengths. This raises the possibility that the inner regions of ULDM halos are more complex and diverse than previously assumed.

Cosmic voids are low-mass-density regions on intergalactic scales. They are where cosmic expansion and acceleration are most dominant, important places to understand and analyze for cosmology. This entry summarises theoretical underpinnings of cosmic voids, and explores several observational aspects, statistics and applications of voids. The density profiles, velocity profiles, evolution history and the abundances of voids are shown to encode information about cosmology, including the sum of neutrino masses and the law of gravity. These properties manifest themselves into a wide range of observables, including the void distribution function, redshift-space distortions, gravitational lensing and their imprints on the cosmic-microwave background. We explain how each of these observables work, and summarise their applications in observations. We also comment on the possible impact of a local void on the interpretations of the expansion of the Universe, and discuss opportunities and challenges for the research subject of cosmic voids.

We present DeepVoid, an application of deep learning trained on a physical definition of cosmic voids to detect voids in density fields and galaxy distributions. By semantically segmenting the IllustrisTNG simulation volume using the tidal tensor, we train a deep convolutional neural network to classify local structure using a U-Net architecture for training and prediction. The model achieves a void F1 score of 0.96 and a Matthews correlation coefficient over all structural classes of 0.81 for dark matter particles in IllustrisTNG with interparticle spacing of λ = 0.33 h ^−1 Mpc. We then apply the machine learning technique of curriculum learning to enable the model to classify structure in data with significantly larger intertracer separation. At the highest tracer separation tested, λ = 10 h ^−1 Mpc, the model achieves a void F1 score of 0.89 and a Matthews correlation coefficient of 0.6 on IllustrisTNG subhalos.

We discuss what we call halo or galaxy root systems, collections of particle pathlines that show the infall of matter from the initial uniform distribution into a collapsed structure. The matter clumps as it falls in; projected through time, it produces filamentary density enhancements analogous to tree roots and branches, or branching river networks. This relates to the larger-scale cosmic web, but is defined locally about one of its nodes: a physical, geometric version of a merger tree. We find dark-matter-halo root systems on average to exhibit more roots and root branches for the largest cluster haloes than in small haloes, indicating their more complicated assembly, even in dark matter without gas physics. Also, we find that high spin manifests as curvier roots, and that many have mass contributions that start far away from central, compact protohalo cores. Root systems also show sensitivity to anisotropic infall, which we see some evidence for in a simulation with rather low box size.

Accurate modeling for the evolution of the baryon acoustic oscillation (BAO) is essential for using it as a standard ruler to probe cosmology. We explore the nonlinearity of the BAO in different environments using the density-split statistics and compare them to the case of the conventional two-point correlation function (2PCF). We detect density-dependent shifts for the position of the BAO with respect to its linear version using halos from N -body simulations. Around low/high-densities, the scale of the BAO expands/contracts due to nonlinear peculiar velocities. As the simulation evolves from redshift 1 to 0, the difference in the magnitude of the shifts between high- and low-dense regions increases from the subpercent to the percent level. The width of the BAO around high density regions increases as the universe evolves, similar to the known broadening of the BAO in the 2PCF due to nonlinear evolution. In contrast, the width is smaller and stable for low density regions. We discuss possible implications for the reconstructions of the BAO in light of our results.

ABSTRACT We present a scheme based on artificial neural networks (ANNs) to estimate the line-of-sight velocities of individual galaxies from an observed redshift–space galaxy distribution. We find an estimate of the peculiar velocity at a galaxy based on galaxy counts and barycentres in shells around it. By training the network with environmental characteristics, such as the total mass and mass centre within each shell surrounding every galaxy in redshift space, our ANN model can accurately predict the line-of-sight velocity of each individual galaxy. When this velocity is used to eliminate the RSD effect, the two-point correlation function (TPCF) in real space can be recovered with an accuracy better than 1 per cent at s > 8 $\, h^{-1}\, \mathrm{Mpc}$, and 4 per cent on all scales compared to ground truth. The real-space power spectrum can be recovered within 3 per cent on k< 0.5 $\, \mathrm{Mpc}^{-1}\, h$, and less than 5 per cent for all k modes. The quadrupole moment of the TPCF or power spectrum is almost zero down to s = 10 $\, h^{-1}\, \mathrm{Mpc}$ or all k modes, indicating an effective correction of the spatial anisotropy caused by the RSD effect. We demonstrate that on large scales, without additional training with new data, our network is adaptable to different galaxy formation models, different cosmological models, and mock galaxy samples at high-redshifts and high biases, achieving less than 10 per cent error for scales greater than 15 $\, h^{-1}\, \mathrm{Mpc}$. As it is sensitive to large-scale densities, it does not manage to remove Fingers of God in large clusters, but works remarkably well at recovering real-space galaxy positions elsewhere. Our scheme provides a novel way to predict the peculiar velocity of individual galaxies, to eliminate the RSD effect directly in future large galaxy surveys, and to reconstruct the three-dimensional cosmic velocity field accurately.

The spin of intergalactic filaments has been predicted from simulations, and supported by tentative evidence from redshift-space filament shapes in a galaxy redshift survey: generally, a filament is redshifted on one side of its axis, and blueshifted on the other. Here, we investigate whether filament spins could have a measurable kinetic Sunyaev-Zel'dovich (kSZ) signal, from CMB photons being scattered by moving ionised gas; this pure velocity information is complementary to filament redshift-space shapes. We propose to measure the kSZ dipole by combining galaxy redshift surveys with CMB experiments. We base our S/N analyses first on an existing filament catalogue, and its combination with Planck data. We then investigate the detectability of the kSZ dipole using the combination of DESI or SKA-2 with next-stage CMB experiments. We find that the gas halos of filament galaxies co-rotating with filaments induce a stronger kSZ dipole signal than that from the diffuse filamentary gas, but both signals seem too small to be detected in near-term surveys such as DESI+future CMB experiments. But the combination of SKA-2 with future CMB experiments could give a more than 10σ detection. The gain comes mainly from an increased area overlap and an increased number of filaments, but also the low noise and high resolution in future CMB experiments are important to capture signals from filaments small on the sky. Successful detection of the signals may help to find the gravitomagnetic effect in large-scale structure and advance our understanding of baryons in the cosmic web.

Galaxies and their dark-matter halos are commonly presupposed to spin. But it is an open question how this spin manifests in halos and soliton cores made of scalar dark matter (SDM, including fuzzy/wave/ultralight-axion dark matter). One way spin could manifest in a necessarily irrotational SDM velocity field is with a vortex. But recent results have cast doubt on this scenario, finding that vortices are generally unstable except with substantial repulsive self-interaction. In this paper, we introduce an alternative route to stability: in both (non-relativistic) analytic calculations and simulations, a black hole or other central mass at least as massive as a soliton can stabilize a vortex within it. This conclusion may also apply to AU-scale halos bound to the sun and stellar-mass-scale Bose stars.

The Milky Way and the Local Sheet form a peculiar galaxy system in terms of the unusually low velocity dispersion in our neighbourhood and the seemingly high mass of the Milky Way for such an environment. Using the TNG300 simulation we searched for Milky Way analogues (MWA) located in cosmological walls with velocity dispersion in their local Hubble flow similar to the one observed around our galaxy. We find that MWAs in Local-Sheet analogues are rare, with one per (160-200 Mpc)^3 volume. We find that a Sheet-like cold environment preserves, amplifies, or simplifies environmental effects on the angular momentum of galaxies. In such sheets, there are particularly strong alignments between the sheet and galaxy spins; also, these galaxies have low spin parameters. These both may relate to a lack of mergers since wall formation. We hope our results will bring awareness of the atypical nature of the Milky Way-Local Sheet system. Wrongly extrapolating local observations without a full consideration of the effect of our cosmic environment can lead to a Copernican bias in understanding the formation and evolution of the Milky Way and the nearby Universe.

According to the standard model of cosmology, the arrangement of matter in the cosmos on scales much larger than galaxies is entirely specified by the initial conditions laid down during inflation. But zooming in by dozens of orders of magnitude to microscopic (and human?) scales, quantum randomness reigns, independent of the initial conditions. Where is the boundary of determinism, and how does that interplay with chaos? Here, we make a first attempt at answering this question in an astronomical context, including currently understood processes. The boundary is a function, at least, of length scale, position, and matter type (dark matter being more simply predictable). In intergalactic voids, the primordial pattern of density fluctuations is largely preserved. But we argue that within galaxies, the conditions are at minimum chaotic, and may even be influenced by non-primordial information, or randomness independent of the initial conditions. Randomness could be supplied by events such as supernovae and jets from active galactic nuclei (AGN) and other accretion disks, which, with the help of chaotic dynamics, could broadcast any possible microscopic randomness to larger scales, eventually throughout a galaxy. This may be generated or amplified by a recently investigated process called spontaneous stochasticity, or effective randomness in turbulent systems arising from arbitrarily small perturbations.

Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 1024^3 dark matter particles in a box of length 1 Gpc/h, 64 snapshots of particle data and halo catalogs, and 505 time steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large datasets public and computationally accessible.

Matter in the Universe is arranged in a cosmic web, with a filament of matter typically connecting each neighbouring galaxy pair, separated by tens of millions of light-years. A quadrupolar pattern of the spin field around filaments is known to influence the spins of galaxies and haloes near them, but it remains unknown whether filaments themselves spin. Here, we measure dark-matter velocities around filaments in cosmological simulations, finding that matter generally rotates around them, much faster than around a randomly located axis. It also exhibits some coherence along the filament. The net rotational component is comparable to, and often dominant over, the known quadrupolar flow. The evidence of net rotations revises previous emphasis on a quadrupolar spin field around filaments. The full picture of rotation in the cosmic web is more complicated and multiscale than a network of spinning filamentary rods, but we argue that filament rotation is substantial enough to be an essential part of the picture. It is likely that the longest coherently rotating objects in the Universe are filaments. Also, we speculate that this rotation could provide a mechanism to generate or amplify intergalactic magnetic fields in filaments.

ABSTRACT Dark matter (DM) as a Bose–Einstein condensate, such as the axionic scalar field particles of String Theory, can explain the coldness of DM on large scales. Pioneering simulations in this context predict a rich wave-like structure, with a ground state soliton core in every galaxy surrounded by a halo of excited states that interfere on the de Broglie scale. This de Broglie scale is largest for the low-mass galaxies as momentum is lower, providing a simple explanation for the wide cores of dwarf spheroidal galaxies. Here we extend these ‘wave dark matter’ (ψDM) predictions to the newly discovered class of ‘ultra-diffuse galaxies’ (UDG) that resemble dwarf spheroidal galaxies but with more extended stellar profiles. Currently, the best-studied example, ‘Dragon Fly 44’ (DF44), has a uniform velocity dispersion of ≃33 km s−1, extending to at least 3 kpc, that we show is reproduced by our ψDM simulations with a soliton radius of ≃0.5 kpc. In the ψDM context, we show that relatively flat dispersion profile of DF44 lies between massive galaxies with compact dense solitons, as may be present in the Milky Way on a scale of 100 pc and lower mass galaxies where the velocity dispersion declines centrally within a wide, low-density soliton, like Antlia II, of radius 3 kpc.

ABSTRACT We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO) signal, which contains key cosmological information, based on deep convolutional neural networks (CNN). Trained with almost no fine tuning, the network can recover large-scale modes accurately in the test set: the correlation coefficient between the true and reconstructed initial conditions reaches $90{{\ \rm per\ cent}}$ at $k\le 0.2 \, h\mathrm{Mpc}^{-1}$, which can lead to significant improvements of the BAO signal-to-noise ratio down to $k\simeq 0.4\, h\mathrm{Mpc}^{-1}$. Since this new scheme is based on the configuration-space density field in sub-boxes, it is local and less affected by survey boundaries than the standard reconstruction method, as our tests confirm. We find that the network trained in one cosmology is able to reconstruct BAO peaks in the others, i.e. recovering information lost to non-linearity independent of cosmology. The accuracy of recovered BAO peak positions is far less than that caused by the difference in the cosmology models for training and testing, suggesting that different models can be distinguished efficiently in our scheme. It is very promising that our scheme provides a different new way to extract the cosmological information from the ongoing and future large galaxy surveys.

Lagrangian algorithms to simulate the evolution of cold dark matter (CDM) are invaluable tools to generate large suites of mock halo catalogues. In this paper, we first show that the main limitation of current semi-analytical schemes to simulate the displacement of CDM is their inability to model the evolution of overdensities in the initial density field, a limit that can be circumvented by detecting halo particles in the initial conditions. We thus propose `MUltiscale Spherical Collapse Lagrangian Evolution Using Press-Schechter' (muscle-ups), a new scheme that reproduces the results from Lagrangian perturbation theory on large scales, while improving the modelling of overdensities on small scales. In muscle-ups, we adapt the extended Press and Schechter (EPS) formalism to Lagrangian algorithms of the displacement field. For regions exceeding a collapse threshold in the density smoothed at a radius $R$, we consider all particles within a radius $R$ collapsed. Exploiting a multi-scale smoothing of the initial density, we build a halo catalogue on the fly by optimizing the selection of halo candidates. This allows us to generate a density field with a halo mass function that matches one measured in $N$-body simulations. We further explicitly gather particles in each halo together in a profile, providing a numerical, Lagrangian-based implementation of the halo model. Compared to previous semi-analytical Lagrangian methods, we find that muscle-ups improves the recovery of the statistics of the density field at the level of the probability density function (PDF), the power spectrum, and the cross correlation with the $N$-body result.

Are there parallels between the furthest reaches of our universe, and the foundations of thought, awareness, perception, and emotion? What are the connections between the webs and structures that define both? What are the differences? "As Above As Below" was an exhibition that examined these questions. It consisted of six artworks, each of them the product of a collaboration that included at least one artist, astrophysicist, and neuroscientist. The installations explored new parallels between intergalactic and neuronal networks through media such as digital projection, virtual reality, and interactive multimedia, and served to illustrate diverse collaboration practices and ways to communicate across very different fields.

The standard explanation for galaxy spin starts with the tidal-torque theory (TTT), in which an ellipsoidal dark-matter protohalo, which comes to host the galaxy, is torqued up by the tidal gravitational field around it. We discuss a complementary picture, using the relatively familiar velocity field, instead of the tidal field, whose intuitive connection to the surrounding, possibly faraway matter arrangement is more obscure. In this 'spin from primordial inner motions' (SPIM) concept, implicit in TTT derivations but not previously emphasized, the angular momentum from the gravity-sourced velocity field inside a protohalo largely cancels out, but has some excess from the aspherical outskirts. At first, the net spin scales according to linear theory, a sort of comoving conservation of familiar angular momentum. Then, at collapse, it is conserved in physical coordinates. Small haloes are then typically subject to secondary exchanges of angular momentum. The TTT is useful for analytic estimates. But a literal interpretation of the TTT is inaccurate in detail, without some implicit concepts about smoothing of the velocity and tidal fields. This could lead to misconceptions, for those first learning about how galaxies come to spin. Protohaloes are not perfectly ellipsoidal and do not uniformly torque up, as in a naive interpretation of the TTT; their inner velocity fields retain substantial dispersion. Furthermore, quantitatively, given initial conditions and protohalo boundaries, SPIM is more direct and accurate than the TTT to predict halo spins. We also discuss how SPIM applies to rotating filaments, and the relation between halo mass and spin, in which the total spin of a halo can be thought of as a sum of random contributions.

We present a public code to generate random fields with an arbitrary probability distribution function (PDF) and an arbitrary correlation function. The algorithm is cosmology-independent, applicable to any stationary stochastic process over a three dimensional grid. We implement it in the case of the matter density field, showing its benefits over the lognormal approximation, which is often used in cosmology for generation of mock catalogues. We find that the covariance of the power spectrum from the new fast realizations is more accurate than that from a lognormal model. As a proof of concept, we also apply the new simulation scheme to the divergence of the Lagrangian displacement field. We find that information from the correlation function and the PDF of the displacement-divergence provides modest improvement over other standard analytical techniques to describe the particle field in the simulation. This suggests that further progress in this direction should come from multi-scale or non-local properties of the initial matter distribution.

The Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) is a 101 orbit + 101 parallel Cycle 25 Hubble Space Telescope (HST) Treasury program taking data from 2018 to 2020. BUFFALO will expand existing coverage of the Hubble Frontier Fields (HFF) in Wide Field Camera 3/IR F105W, F125W, and F160W and Advanced Camera for Surveys/WFC F606W and F814W around each of the six HFF clusters and flanking fields. This additional area has not been observed by HST but is already covered by deep multiwavelength data sets, including Spitzer and Chandra. As with the original HFF program, BUFFALO is designed to take advantage of gravitational lensing from massive clusters to simultaneously find high-redshift galaxies that would otherwise lie below HST detection limits and model foreground clusters to study the properties of dark matter and galaxy assembly. The expanded area will provide the first opportunity to study both cosmic variance at high redshift and galaxy assembly in the outskirts of the large HFF clusters. Five additional orbits are reserved for transient follow-up. BUFFALO data including mosaics, value-added catalogs, and cluster mass distribution models will be released via MAST on a regular basis as the observations and analysis are completed for the six individual clusters.

We propose the Cosmic Web Detachment (CWD) model, a framework to interpret the star-formation history of galaxies in a cosmological context. The CWD model unifies several starvation mechanisms known to disrupt or stop star formation into one single physical framework. Galaxies begin accreting star-forming gas at early times via a network of primordial filaments, simply related to the pattern of density fluctuations in the initial conditions. But when streams cross on intergalactic scales, this pattern is disrupted, and the galaxy detaches from its primordial filaments, ending the accretion of cold gas. We argue that CWD encompasses known external processes halting star formation, such as harassment, strangulation and starvation. On top of these external processes, internal feedback processes such as AGN contribute to stop in star formation as well. We introduce a simple formalism to identify CWD events and quenched galaxies in dark-matter N-body simulations. With it we reproduce and explain, in the context of CWD, several observations including downsizing, the cosmic star formation rate history, the galaxy mass-color diagram and the dependence of the fraction of red galaxies with mass and local density.