Large-scale Matter Distribution Research Articles

The SRG/eROSITA All-Sky Survey

Context. The spatial distribution of galaxy clusters provides a reliable tracer of the large-scale distribution of matter in the Universe. The clustering signal depends on intrinsic cluster properties and cosmological parameters. Aims. The ability of eROSITA on board Spectrum-Roentgen-Gamma (SRG) to discover galaxy clusters allows the association of extended X-ray emission with dark matter haloes to be probed. We measured the projected two-point correlation function to study the occupation of dark matter haloes by clusters and groups detected by the first eROSITA all-sky survey (eRASS1). Methods. We created five volume-limited samples probing clusters with different redshifts and X-ray luminosity values. We interpreted the correlation function with halo occupation distribution (HOD) and halo abundance matching (HAM) models. We simultaneously fit the cosmological parameters and halo bias of a flux-limited sample of 6493 clusters with purity > 96%. Results. We obtained a detailed view of the halo occupation for eRASS1 clusters. The fainter population at low redshift (S0: L̄X = 4.63 × 1043 erg s−1, 0.1 < z < 0.2) is the least biased compared to dark matter, with b = 2.95 ± 0.21. The brightest clusters up to higher redshift (S4: L̄X = 1.77 × 1044 erg s−1, 0.1 < z < 0.6) exhibit a higher bias b = 4.34 ± 0.62. Satellite groups are rare, with a satellite fraction < 14.9% (8.1) for the S0 (S4) sample. We combined the HOD prediction with a HAM procedure to constrain the scaling relation between LX and mass in a new way, and find a scatter of ⟨σLx⟩ = 0.36. We obtain cosmological constraints for the physical cold dark matter density ωc = 0.12−0.02+0.03 and an average halo bias b = 3.63−0.85+1.02. Conclusions. We modelled the clustering of galaxy clusters with a HOD approach for the first time, paving the way for future studies combining eROSITA with 4MOST, SDSS, Euclid, Rubin, and DESI to unravel the cluster distribution in the Universe.

Atomic hydrogen shows its true colours: correlations between H i and galaxy colour in simulations

ABSTRACT Intensity mapping experiments are beginning to measure the spatial distribution of neutral atomic hydrogen H i to constrain cosmological parameters and the large-scale distribution of matter. However, models of the behaviour of H i as a tracer of matter are complicated by galaxy evolution. In this work, we examine the clustering of H i in relation to galaxy colour, stellar mass, and H i mass in IllustrisTNG at z = 0, 0.5, and 1. We compare the H i-red and H i-blue galaxy cross-power spectra, finding that H i-red has an amplitude 1.5 times greater than H i-blue at large scales. The cross-power spectra intersect at ≈3 Mpc in real space and ≈10 Mpc in redshift space, consistent with z ≈ 0 observations. We show that H i clustering increases with galaxy H i mass and depends weakly on detection limits in the range MH i ≤ 108 M⊙. In terms of M⋆, we find massive blue galaxies cluster more than less massive ones. Massive red galaxies, however, cluster the weakest amongst red galaxies. These opposing trends arise from central-satellite compositions. Despite these M⋆ trends, we find that the cross-power spectra are largely insensitive to detection limits in galaxy surveys. Counter-intuitively, all auto and cross-power spectra for red and blue galaxies and H i decrease with time at all scales. We demonstrate that processes associated with quenching contribute to this trend. The complex interplay between H i and galaxies underscores the importance of understanding baryonic effects when interpreting the large-scale clustering of H i, blue, and red galaxies at z ≤ 1.

Constraints on the origin of the radio synchrotron background via angular correlations

ABSTRACT The origin of the radio synchrotron background (RSB) is currently unknown. Its understanding might have profound implications in fundamental physics or might reveal a new class of radio emitters. In this work, we consider the scenario in which the RSB is due to extragalactic radio sources and measure the angular cross-correlation of Low-Frequency Array (LOFAR) images of the diffuse radio sky with matter tracers at different redshifts, provided by galaxy catalogues and cosmic microwave background lensing. We compare these measured cross-correlations to those expected for models of RSB sources. We find that low-redshift populations of discrete sources are excluded by the data, while higher redshift explanations are compatible with available observations. We also conclude that at least 20 per cent of the RSB surface brightness level must originate from populations tracing the large-scale distribution of matter in the Universe, indicating that at least this fraction of the RSB is of extragalactic origin. Future measurements of the correlation between the RSB and tracers of high-redshift sources will be crucial to constraining the source population of the RSB.

The phase-space distribution of the M 81 satellite system

The spatial distribution of dwarf galaxies around their host galaxies is a critical test for the standard model of cosmology because it probes the dynamics of dark matter halos and is independent of the internal baryonic processes of galaxies. Comoving planes of satellites have been found around the Milky Way, the Andromeda galaxy, and the nearby Cen A galaxy, which seems to be at odds with the standard model of galaxy formation. Another nearby galaxy group, with a putative flattened distribution of dwarf galaxies, is the M 81 group. In this paper, we present a quantitative analysis of the distribution of the M 81 satellites using a Hough transform to detect linear structures. Using this method, we confirm a flattened distribution of the dwarf galaxies. Depending on the morphological type, we find a minor-to-major axis ratio of the satellite distribution of 0.5 (all types) or 0.3 (dSph), which is in line with previous results for the M 81 group. Comparing the orientation of this flattened structure in 3D with the surrounding large-scale matter distribution, we find a strong alignment with the local sheet and the planes of satellites around the Andromeda galaxy and Cen A. Furthermore, the satellite system seems to be lopsided. Employing line-of-sight velocities for a subsample of the dwarfs, we find no signal of corotation. Comparing the flattening and motion of the M 81 dwarf galaxy system with TNG50 of the IllustrisTNG suite we find good agreement between observations and simulations, but caution that i) velocity information of half of the satellite population is still missing, ii) current velocities mainly come from dwarf irregulars clustered around NGC 3077, which may indicate an infall of a dwarf galaxy group, and iii) some of the dwarfs in our sample may be tidal dwarf galaxies. From the missing velocities, we predict that the observed frequency within IllustrisTNG may still range between 2 to 29%. Any final conclusions about the agreement or disagreement with cosmological models needs to wait for a more complete picture of the dwarf galaxy system.

Cosmic Tidal Reconstruction in Redshift Space

Gravitational coupling between large- and small-scale density perturbations leads to anisotropic distortions to local small-scale matter fluctuations. Such local anisotropic distortions can be used to reconstruct large-scale matter distribution, known as tidal reconstruction. In this paper, we apply the tidal reconstruction methods to simulated galaxies in redshift space. We find that redshift-space distortions (RSDs) lead to anisotropic reconstruction results. While the reconstructed radial modes are more noisy mainly due to the small-scale velocity dispersion, the transverse modes are still reconstructed with high fidelity, and well correlated with the original large-scale density modes. The bias of the reconstructed field at large scales shows a simple angular dependence, which can be described by a form similar to that of the linear RSD. The noise power spectrum is nearly isotropic and scale independent on large scales. This makes the reconstructed tide fields an ideal tracer for cosmic variance cancellation and multi-tracer analysis and has profound implications for future 21 cm intensity mapping surveys.

The observed number counts in luminosity distance space

Next generation surveys will provide us with an unprecedented number of detections of supernovae Type Ia and gravitational wave merger events. Cross-correlations of such objects offer novel and powerful insights into the large-scale distribution of matter in the universe. Both of these sources carry information on their luminosity distance, but remain uninformative about their redshifts; hence their clustering analyses and cross-correlations need to be carried out in luminosity distance space, as opposed to redshift space. In this paper, we calculate the full expression for the number count fluctuation in terms of a perturbation to the observed luminosity distance. We find the expression to differ significantly from the one commonly used in redshift space. Furthermore, we present a comparison of the number count angular power spectra between luminosity distance and redshift spaces. We see a wide divergence between the two at large scales, and we note that lensing is the main contribution to such differences. On such scales and at higher redshifts the difference between the angular power spectra in luminosity distance and redshift spaces can be roughly 50%. We also investigate cross-correlating different redshift bins using different tracers, i.e. one in luminosity distance space and one in redshift, simulating the cross-correlation angular power spectrum between background gravitational waves/supernovae and foreground galaxies. Finally, we show that in a cosmic variance limited survey, the relativistic corrections to the density-only term ought to be included.

Theoretical Systematics in Testing the Cosmological Principle with the Kinematic Quasar Dipole

The Cosmological Principle (CP) is part of the foundation that underpins the standard model of the Universe. In the era of precision cosmology, when stress tests of the standard model are uncovering various tensions and possible anomalies, it is critical to check the viability of this principle. A key test is the consistency between the kinematic dipoles of the cosmic microwave background and of the large-scale matter distribution. Results using radio continuum and quasar samples indicate a rough agreement in the directions of the two dipoles, but a larger than expected amplitude of the matter dipole. The resulting tension with the radiation dipole has been estimated at ∼5σ for some cases, suggesting a potential new cosmological tension and a possible violation of the CP. However, the standard formalism for predicting the dipole in the two-dimensional projection of sources overlooks possible evolution effects in the luminosity function. In fact, radial information from the luminosity function is necessary for a correct projection of the three-dimensional source distribution. Using a variety of current models of the quasar luminosity function, we show that neglecting redshift evolution can significantly overestimate the relative velocity amplitude. While the models we investigate are consistent with each other and with current data, the dipole derived from these, which depends on derivatives of the luminosity function, can disagree by more than 3σ. This theoretical systematic bias needs to be resolved before robust conclusions can be made about a new cosmic tension.

Research progress, challenges, and prospects of PM2.5 concentration estimation using satellite data

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM2.5) due to their low cost, wide coverage, and all-weather capability. Estimation of PM2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons ( p < 0.01) and different spatial resolutions ( p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.

Scaling relations in the network of voids

ABSTRACT The large-scale distribution of matter in the Universe forms a network of clusters, filaments, and walls enclosing large empty voids. Voids in turn can be described as a cellular system in which voids/cells define dynamically distinct regions. Cellular systems arising from a variety of physical and biological processes have been observed to closely follow scaling laws relating their geometry, topology, and dynamics. These scaling laws have never been studied for cosmological voids, the largest known cellular system. Using a cosmological N-body simulation, we present a study of the scaling relations of the network of voids, extending their validity by over 30 orders of magnitude in scale with respect to other known cellular systems.

CosNAS: Enhancing estimation on cosmological parameters via neural architecture search

A great challenge of cosmology is estimating the cosmological parameters of the universe. With the development of deep learning, scientists adopt 3D deep neural networks to estimate cosmological parameters from the large-scale dark matter distribution of the universe, but these methods are time-consuming to design and train neural networks. While neural architecture search is an emerging approach to estimate cosmological parameters with its capability of automatically designing neural networks, the 3D operations on a 3D dataset prohibit the usage of traditional neural architecture search methods, due to its overwhelming time and memory consumption. To tackle these issues, we propose an efficient method, CosNAS, that can automatically design neural networks with 2D operations to estimate the cosmological parameters. In addition, processing 3D data with 2D operations will inevitably cause the loss of spatial information, thus we propose an efficient SABlock to retain more 3D spatial information. We also customize a space-focused search space to focus on important information in the dark matter distribution. The experimental results indicate that our estimation of the cosmological parameters Ω, σ and n, can be applied to large-scale 3D dark matter distribution and speedup the network search by 800x. The average relative errors of cosmological parameter estimations are (0.00163, 0.00065, 0.00080), significantly decreasing the average error of estimation by 85.5% compared to previous work.

Cosmic voids and the kinetic analysis

The kinetic approach to the formation of the filaments in the large-scale matter distribution in the Universe is considered within the Vlasov formalism. The structures arise due to the self-consistent dynamics, along with the repulsive term in the modified Newtonian gravity which includes the cosmological constant. That modified gravity enables one to describe the Hubble tension as a result of two flows, the local and global ones. The criteria for formation of non–stationary semi-periodic structures in a system of gravitating particles described by Vlasov–Poisson equations are obtained in the case of that repulsive term. The obtained dispersion relations for the Vlasov equation in the vicinity of the singular point of the modified gravitational potential demonstrate the possibility of the emergence of filaments as coherent complex states of relative equilibrium in non–stationary systems as structures of low dimensions (walls), and voids between them, of scales (diameters) defined by the balance between the gravity and repulsive term of the cosmological constant.

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks.

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies-called polyphorms-that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

Editorial Introduction for 28:1.

In this issue we are pleased to share with you a diverse set of reading materials. Sadly, we mark with an obituary the passing of Julian Miller, a researcher whose loss has been keenly felt within the community of Artificial Life researchers. He shall be sorely missed.On a much brighter note, the second installment of Chris Adami’s column exploring how artificial evolution might facilitate the design of General Intelligence is to be found within the pages of this issue. Adami explains how the indirect encoding of artificial brains to facilitate neuro-evolution might be managed. He discusses approaches to choosing an appropriate neuron, how to connect neurons to create a functioning network, how to train the network, and how the different options scale up to high levels of complexity. Drawing such connections between the techniques of Artificial Life and the concerns of Artificial Intelligence is key (we feel) to enhancing the recognition that embodiment, developmental processes, and evolutionary processes all have a role to play in the emergence of natural intelligence – to overlook this whilst striving for artificial general intelligence is likely problematic.Simon Penny, an artist long engaged in Artificial Life art and robotics, provides for us a critical review of a new book by Sofian Audry, Art in the Age of Machine Learning (MIT Press 2021). The title might seem to be slightly out of line with Artificial Life’s main focus, perhaps even more suited to an AI readership, but, as Penny points out, this isn’t necessarily the case. In fact, by presenting both the practical artistic-technological concerns of the day, and the philosophical issues these raise with respect to agency, creativity and art-making by machines, Audry is in fact delving into areas that should concern us as researchers of Artificial Life.A topic infrequently explored within the pages of this journal is the impact that Artificial Life has on human relationships. In Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots, Paolo Euron enters this space by examining “physical beauty according to the artistic, cultural, and philosophical traditions”, of sexbots. Since Euron focuses on the visual appearance of these humanoid robots, with this article we have adopted a new approach for the Artificial Life journal to widen the perspective. The text is therefore supported by commentaries the editors have sought from alternative points of view. Thomas Arnold provides comment on Euron’s work from the perspective of Human-Robot Interaction by assessing the ethics of sex robots and how concepts of human trust, dignity, and autonomy potentially influence our interactions with such machines. Maria O’Sullivan examines how human interactions with sexbots relate to gender power relations and our expectations and human norms of intimacy and vulnerability. She also considers the very real dangers now widely associated with the commodification of beauty and the potential for moral harm that may result from an increase in the ubiquity or use of sexbots. We hope that you find the article and commentaries thought provoking.The issue also includes five other intriguing research articles.In Computation by Convective Logic Gates and Thermal Communication, Bartlett et al. demonstrate a simulation of an embodied computational system of Boolean NOR gates that can be realised in a convective fluid. This demonstrates that computation can be achieved in relatively simple physical contexts.The article Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution, by Benureau and Tani, flips the usual biological timescales (slow evolution, faster developmental processes) to one where morphological development is slower than evolution, and applies this in the context of robot design. Early in the evolutionary process, robots are ‘babies’, and only later on in evolution have they developed into ‘adults’. The authors investigate this novel approach to evolve diverse gaits.In Effect of Environmental Change Distribution on Artificial Life Simulations, Bullinaria explores the effect of environmental change (modelled as coloured noise) on evolutionary outcomes. He carefully dissects two factors: the average size, and the distribution of changes, to show that results are not as sensitive to noise colour as previously thought.Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks, authored by Elek et al., takes inspiration from slime mould growth to develop an algorithm for reconstructing networks from sparse data. The authors demonstrate their algorithm by applying it to a case study rarely seen in the Artificial Life arena: cosmological data sets of the large-scale distribution of gas and dark matter in the universe.In From Dynamics to Novelty: An Agent-Based Model of the Economic System, Recio et al. explicitly include time evolution in a new model of a complex self-organising economy. They realise their model in an agent-based simulation of an economy that can generate new technologies and products, and investigate the dynamics of the resulting system.We hope you enjoy the issue!

COWS: a filament finder for Hessian cosmic web identifiers

ABSTRACT The large-scale galaxy and matter distribution is often described by means of the cosmic web made up of voids, sheets, filaments, and knots. Many different recipes exist for identifying this cosmic web. Here, we focus on a sub-class of cosmic web identifiers, based on the analysis of the Hessian matrix, and proposed a method, called COsmic Web Skeleton (COWS), of separating a set of filaments cells into an ensemble of individual discreet filaments. Specifically, a thinning algorithm is applied to velocity shear tensor-based cosmic web (V-web) to identify the spine of the filaments. This results in a set of filaments with well-defined end point and length. It is confirmed that these sit at local density ridges and align with the appropriate direction defined by the underlying velocity field. The radial density profile of these curved cylindrical filaments, as well as the distribution of their lengths is also examined. The robustness of all results is checked against resolution and the V-web threshold. The code for the COWS method has been made publicly available.

Skewness as a test of dark energy perturbations

We investigate the role played by dark energy perturbations in the skewness ${S}_{3}$ of large-scale matter distribution. We consider a two-fluid universe composed by matter and dark energy, with perturbations in both components, and we estimate numerically the skewness of the matter density field as a function of the dark energy parameters. We characterize today's ${S}_{3}$ value for quintessence and phantom dark energy cosmologies as well as its dependence on the matter density parameter ${\mathrm{\ensuremath{\Omega}}}_{m0}$ and the dark energy sound speed ${c}_{s}^{2}$ with accurate numerical fitting. These fits can be used to test cosmology against future high quality data on large scale structure.

Cosmic Tidal Reconstruction with Halo Fields

Abstract The gravitational coupling between large-scale perturbations and small-scale perturbations leads to anisotropic distortions of the small-scale matter distribution. The measured local small-scale power spectrum can thus be used to infer the large-scale matter distribution. In this paper, we present a new tidal reconstruction algorithm for reconstructing large-scale modes using the full three-dimensional tidal shear information. We apply it to simulated dark matter halo fields and find the reconstructed large-scale density field correlates well with the original matter density field on large scales, improving upon the previous tidal reconstruction method, which only uses two transverse shear fields. This has profound implications for recovering lost 21 cm radial modes due to foreground subtraction and constraining primordial non-Gaussianity using the multitracer method with future cosmological surveys.

Direct measurement of the distribution of dark matter with strongly lensed gravitational waves

In this Letter, we present a new idea of probing the distribution of dark matter exhibiting elastic and velocity-independent self-interactions. These interactions might be revealed in multiple measurements of strongly lensed gravitational waves, which can be observationally explored to determine the strength of self-scatterings. Specifically, each individual galactic-scale strong-lensing system whose source is a coalescing compact binary emitting gravitational waves will provide a model-independent measurement of the shear viscosity of dark matter along the line of sight. These individual measurements could be a probe of large-scale distribution of dark matter and its properties. Our results indicate that with 10–1000 strongly lensed gravitational waves from ET and DECIGO, robust constraints on the large-scale distribution of self-interacting dark matter might be produced. More stringent limits on the dark matter scattering cross-section per unit mass (σχ/mχ) relevant to galaxy and cluster scales are also expected, compared with the conservative estimates obtained in the electromagnetic domain. Finally, we discuss the effectiveness of our method in the context of self-interacting dark matter particle physics.

The Cosmic Large-Scale Structure in X-rays (CLASSIX) Cluster Survey

It is important to map the large-scale matter distribution in the local Universe for cosmological studies, such as the tracing of the large-scale peculiar velocity flow, the characterisation of the environment for different astronomical objects, and for precision measurements of cosmological parameters. We used X-ray luminous clusters to map this matter distribution and find that about 51% of the groups and clusters are members of superclusters which occupy only a few percent of the volume. In this paper we provide a detailed description of these large-scale structures. With a friends-to-friends algorithm, we find eight superclusters with a cluster overdensity ratio of at least two with five or more galaxy group and cluster members in the cosmic volume out to z = 0.03. The four most prominent ones are the Perseus-Pisces, the Centaurus, the Coma, and the Hercules supercluster, with lengths from about 40 to over 100 Mpc and estimated masses of 0.6 − 2.2 × 1016 M⊙. The largest of these structures is the Perseus-Pisces supercluster. The four smaller superclusters include the Local and the Abell 400 supercluster and two superclusters in the constellations Sagittarius and Lacerta. We provide detailed maps, member catalogues, and physical descriptions of the eight superclusters. By constructing superclusters with a range of cluster sub-samples with different lower X-ray luminosity limits, we show that the main structures are always reliably recovered.

Testing the Strong Equivalence Principle. II. Relating the External Field Effect in Galaxy Rotation Curves to the Large-scale Structure of the Universe

Abstract Theories of modified gravity generically violate the strong equivalence principle, so that the internal dynamics of a self-gravitating system in freefall depends on the strength of the external gravitational field (the external field effect). We fit rotation curves (RCs) from the SPARC database with a model inspired by Milgromian dynamics (MOND), which relates the outer shape of an RC to the external Newtonian field from the large-scale baryonic matter distribution through a dimensionless parameter e N. We obtain a > 4σ statistical detection of the external field effect (i.e. e N > 0 on average), confirming previous results. We then locate the SPARC galaxies in the cosmic web of the nearby universe and find a striking contrast in the fitted e N values for galaxies in underdense versus overdense regions. Galaxies in an underdense region between 22 and 45 Mpc from the celestial axis in the northern sky have RC fits consistent with e N ≃ 0, while those in overdense regions adjacent to the CfA2 Great Wall and the Perseus−Pisces Supercluster return e N that are a factor of two larger than the median for SPARC galaxies. We also calculate independent estimates of e N from galaxy survey data and find that they agree with the e N inferred from the RCs within the uncertainties, the chief uncertainty being the spatial distribution of baryons not contained in galaxies or clusters.

Not all peaks are created equal: the early growth of supermassive black holes

ABSTRACT In this work, we use the constrained Gaussian realization technique to study the early growth of supermassive black holes (SMBHs) in cosmological hydrodynamic simulations, exploring its relationship with features of the initial density peaks on large scales, ∼ 1 h−1Mpc. Our constrained simulations of volume (20 h−1Mpc)3 successfully reconstruct the large-scale structure as well as the black hole growth for the hosts of the rare 109 M⊙ SMBHs found in the bluetides simulation at z ∼ 7. We run a set of simulations with constrained initial conditions by imposing a $5 \sigma _0(R_\mathrm{G})$ peak on the scale of $\\ R_\mathrm{ G} = 1$ h−1Mpc varying different peak features, such as the shape and compactness as well as the tidal field surrounding the peak. We find that initial density peaks with high compactness and low tidal field induce the most rapid BH growth at early epochs. This is because compact density peaks with a more spherical large-scale matter distribution lead to the formation of the highest gas inflows (mostly radial) in the centres of haloes which boost the early BH accretion. Moreover, such initially compact density peaks in low tidal field regions also lead to a more compact BH host galaxy morphology. Our findings can help explain the tight correlation between BH growth and host galaxy compactness seen in observations.