Hierarchical Merging Research Articles

Quasi-two-dimensionality of three-dimensional, magnetically dominated, decaying turbulence

Decaying magnetohydrodynamic (MHD) turbulence is important in various astrophysical contexts, including early universe magnetic fields, star formation, turbulence in galaxy clusters, magnetospheres and solar corona. Previously known in the nonhelical case of magnetically dominated decaying turbulence, we show that magnetic reconnection is important also in the fully helical case and is likely the agent responsible for the inverse transfer of energy. Again, in the fully helical case, we find that there is a similarity in power law decay exponents in both 2.5D and 3D simulations. To understand this intriguing similarity, we investigate the possible quasi-two-dimensionalization of the 3D system. We perform Minkowski functional analysis and find that the characteristic length scales of a typical magnetic structure in the system are widely different, suggesting the existence of local anisotropies. Finally, we provide a quasi-two-dimensional hierarchical merger model which recovers the relevant power law scalings. In the nonhelical case, we show that a helicity-based invariant cannot constrain the system, and the best candidate is still anastrophy or vector potential squared, which is consistent with the quasi-two-dimensionalization of the system.

Resolving the Stellar-Collapse and Hierarchical-Merger Origins of the Coalescing Black Holes.

Spin and mass properties provide essential clues in distinguishing the origins of coalescing black holes (BHs). With a dedicated semiparametric population model for the coalescing binary black holes (BBHs), we identify two distinct categories of BHs among the GWTC-3 events, which is favored over the one population scenario by a logarithmic Bayes factor (lnB) of 7.5. One category, with a mass ranging from ∼25M_{⊙} to ∼80M_{⊙}, is distinguished by the high spin magnitudes (∼0.75) and consistent with the hierarchical merger origin. The other category, characterized by low spins, has a sharp mass cutoff at ∼40M_{⊙}, which is natural for the stellar-collapse origin and in particular the pair-instability explosion of massive stars. We infer the local hierarchical merger rate density as 0.46_{-0.24}^{+0.61} Gpc^{-3} yr^{-1}. Additionally, we find that a fraction of the BBHs has a cosine-spin-tilt-angle distribution concentrated preferentially around 1, and the fully isotropic distribution for spin orientation is disfavored by a lnB of -6.3, suggesting that the isolated field evolution channels are contributing to the total population.

Hierarchical binary black hole mergers in globular clusters: Mass function and evolution with redshift

Hierarchical black hole (BH) mergers are one of the most straightforward mechanisms producing BHs inside and above the pair-instability mass gap. We investigated the impact of globular cluster (GC) evolution on hierarchical mergers, accounting for the uncertainties related to BH mass pairing functions on the predicted primary BH mass, mass ratio, and spin distribution. We find that the evolution of the host GC quenches the hierarchical BH assembly at the third generation, mainly due to cluster expansion powered by a central BH subsystem. Hierarchical mergers match the primary BH mass distribution from GW events for m1 > 50 M⊙ regardless of the assumed BH pairing function. At lower masses, however, different pairing functions lead to dramatically different predictions on the primary BH mass merger-rate density. We find that the primary BH mass distribution evolves with redshift, with a larger contribution from mergers with m1 ≥ 30 M⊙ for z ≥ 2. Finally, we calculate the mixing fraction of binary black holes (BBHs) from GCs and isolated binary systems. Our predictions are very sensitive to the spins, which favor a large fraction (> 0.6) of BBHs born in GCs in order to reproduce misaligned spin observations.

Reconstruction of Binary Black Hole Harmonics in LIGO Using Deep Learning

Gravitational-wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template-based matched filtering method. While this method is optimal for stationary and Gaussian data scenarios, its sensitivity is often affected by nonstationary noise transients in the detectors. Moreover, most of the current searches do not account for the effects of precession of black hole spins and higher-order waveform harmonics, focusing solely on the leading-order quadrupolar modes. This limitation impacts our search for interesting astrophysical sources, such as intermediate-mass black hole binaries and hierarchical mergers. Here we show, for the first time, that deep learning can be used for accurate waveform reconstruction of precessing binary black hole signals with higher-order modes. This approach can also be adapted into a rapid trigger generation algorithm to enhance online searches. Our model, tested on simulated injections in real LIGO noise from the third observing run (2019–2020) achieved a high degree of overlap with injected signals. This accuracy was consistent across a wide range of black hole masses and spin configurations chosen for this study. When applied to real gravitational-wave events, our model's reconstructions achieved between 85% and 98% overlap with those obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses. These results suggest that deep learning is a potent tool for analyzing signals from a diverse catalog of compact binaries.

Great Balls of FIRE

Context. Despite the nearly hundred gravitational-wave detections reported by the LIGO-Virgo-KAGRA Collaboration, the question of the cosmological origin of merging binary black holes (BBHs) remains open. The two main formation channels generally considered are from isolated field binaries or via dynamical assembly in dense star clusters. Aims. Here we focus on understanding the dynamical formation of merging BBHs within massive clusters in galaxies of different masses. Methods. To this end, we applied a new framework to consistently model the formation and evolution of massive star clusters in zoom-in cosmological simulations of galaxies. Each simulation, taken from the FIRE project, provides a realistic star formation environment, with a unique star formation history, that hosts realistic giant molecular clouds that constitute the birthplace of star clusters. Combined with the code for star cluster evolution CMC, we are able to produce populations of dynamically formed merging BBHs across cosmic time in different environments. Results. As the most massive star clusters preferentially form in dense massive clouds of gas, we find that, despite their low metallicities favouring the creation of black holes, low-mass galaxies contain few massive clusters and therefore make a limited contribution to the global production of dynamically formed merging BBHs. Furthermore, we find that massive clusters can host hierarchical BBH mergers with clear, identifiable physical properties. Looking at the evolution of the BBH merger rate in different galaxies, we find strong correlations between BBH mergers and the most extreme episodes of star formation. Finally, we discuss the implications for future LIGO-Virgo-KAGRA gravitational wave observations.

Constraining the LVK AGN channel with black hole spins

ABSTRACT Merging black holes (BHs) are expected to produce remnants with large dimensionless spin parameters (aspin ∼ 0.7). However, gravitational wave (GW) observations with LIGO–Virgo–Kagra (LVK) suggest that merging BHs are consistent with modestly positive but not high spin (aspin ∼ 0.2), causing tension with models suggesting that high-mass mergers are produced by hierarchical merger channels. Some BHs also show evidence for strong in-plane spin components. Here, we point out that spin-down of BHs due to eccentric prograde post-merger orbits within the gas of an active galactic nucleus (AGN) disc can yield BHs with masses in the upper mass gap, but only modestly positive aspin, and thus observations of BHs with low spin do not rule out hierarchical models. We also point out that the fraction of binary black hole (BBH) mergers with significant in-plane spin components is a strong test of interactions between disc BBHs and nuclear spheroid orbiters. Spin magnitude and spin tilt constraints from LVK observations of BBHs are an excellent test of dynamics of BHs in AGN discs, disc properties, and the nuclear clusters interacting with AGNs.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune

BackgroundSingle-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable.ResultsHere, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results—or partitions—on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html.ConclusionsCluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

Impact of gas hardening on the population properties of hierarchical black hole mergers in active galactic nucleus disks

Hierarchical black hole (BH) mergers in active galactic nuclei (AGNs) are unique among formation channels of binary black holes (BBHs) because they are likely associated with electromagnetic counterparts and can efficiently lead to the mass growth of BHs. Here, we explore the impact of gas accretion and migration traps on the evolution of BBHs in AGNs. We have developed a new fast semi-analytic model, that allows us to explore the parameter space while capturing the main physical processes involved. We find that an effective exchange of energy and angular momentum between the BBH and the surrounding gas (i.e., gas hardening) during inspiral greatly enhances the efficiency of hierarchical mergers, leading to the formation of intermediate-mass BHs (up to 104 M⊙) and triggering spin alignment. Moreover, our models with efficient gas hardening show both an anticorrelation between the BBH mass ratio and the effective spin and a correlation between the primary BH mass and the effective spin. In contrast, if gas hardening is inefficient, the hierarchical merger chain is already truncated after the first two or three generations. We compare the BBH population in AGNs with other dynamical channels as well as isolated binary evolution.

Spin Doctors: How to Diagnose a Hierarchical Merger Origin

Gravitational-wave observations provide the unique opportunity of studying black hole formation channels and histories—but only if we can identify their origin. One such formation mechanism is the dynamical synthesis of black hole binaries in dense stellar systems. Given the expected isotropic distribution of component spins of binary black holes in gas-free dynamical environments, the presence of antialigned or in-plane spins with respect to the orbital angular momentum is considered a tell-tale sign of a merger’s dynamical origin. Even in the scenario where birth spins of black holes are low, hierarchical mergers attain large component spins due to the orbital angular momentum of the prior merger. However, measuring such spin configurations is difficult. Here, we quantify the efficacy of the spin parameters encoding aligned-spin (χ eff) and in-plane spin (χ p ) at classifying such hierarchical systems. Using Monte Carlo cluster simulations to generate a realistic distribution of hierarchical merger parameters from globular clusters, we can infer mergers’ χ eff and χ p . The cluster populations are simulated using Advanced LIGO-Virgo sensitivity during the detector network’s third observing period and projections for design sensitivity. Using a “likelihood-ratio”-based statistic, we find that ∼2% of the recovered population by the current gravitational-wave detector network has a statistically significant χ p measurement, whereas no χ eff measurement was capable of confidently determining a system to be antialigned with the orbital angular momentum at current detector sensitivities. These results indicate that measuring spin-precession through χ p is a more detectable signature of hierarchical mergers and dynamical formation than antialigned spins.

Prospects of Identifying Hierarchical Triple Mergers for the Third-generation Ground-based Detectors

A hierarchical triple merger (HTM) constitutes a type of event in which two successive black hole (BH) mergers occur sequentially within the observational window of gravitational-wave (GW) detectors, which has an important role in testing general relativity and studying BH population. In this work, we conduct an analysis to determine the feasibility of identifying HTMs from a large GW event catalog using third-generation ground-based GW detectors. By comparing the Bhattacharyya coefficient that measures the overlap between the posterior distributions of the remnant and progenitor BH parameters, we find that the overlap between the event pair can serve as a preliminary filter, which balances between computational demand and the probability of false alarms. Following this initial, time-efficient, yet less accurate screening, a subset of potential HTM candidates will be retained. These candidates will subsequently be subjected to a more precise, albeit time-intensive, method of joint parameter estimation for verification. Ultimately, this process will enable us to robustly identify HTMs.

Ancestral spin information in gravitational waves from black hole mergers

The heaviest black holes discovered through gravitational waves have masses that are difficult to explain with current standard stellar models. This discrepancy may be due to a series of hierarchical mergers, where the observed black holes are themselves the products of previous mergers. Here we present a method to estimate the masses and spins of previous generations of black holes based on the masses and spins of black holes in a binary. Examining the merger GW190521, we find that assuming black hole spins that are consistent with those of merger remnants will alter the reconstructed ancestral spins when compared to results with uninformed priors. At the same time, the inclusion of black hole spins does not significantly affect the mass distributions of the ancestral black holes.

HEAP: Unsupervised Object Discovery and Localization with Contrastive Grouping

Unsupervised object discovery and localization aims to detect or segment objects in an image without any supervision. Recent efforts have demonstrated a notable potential to identify salient foreground objects by utilizing self-supervised transformer features. However, their scopes only build upon patch-level features within an image, neglecting region/image-level and cross-image relationships at a broader scale. Moreover, these methods cannot differentiate various semantics from multiple instances. To address these problems, we introduce Hierarchical mErging framework via contrAstive grouPing (HEAP). Specifically, a novel lightweight head with cross-attention mechanism is designed to adaptively group intra-image patches into semantically coherent regions based on correlation among self-supervised features. Further, to ensure the distinguishability among various regions, we introduce a region-level contrastive clustering loss to pull closer similar regions across images. Also, an image-level contrastive loss is present to push foreground and background representations apart, with which foreground objects and background are accordingly discovered. HEAP facilitates efficient hierarchical image decomposition, which contributes to more accurate object discovery while also enabling differentiation among objects of various classes. Extensive experimental results on semantic segmentation retrieval, unsupervised object discovery, and saliency detection tasks demonstrate that HEAP achieves state-of-the-art performance.

Using the traditional microscope for mineral grain orientation determination: A prototype image analysis pipeline for optic-axis mapping (POAM).

This paper reports on the development of an open-source image analysis software 'pipeline' dedicated to petrographic microscopy. Using conventional rock thin sections and images from a standard polarising microscope, the pipeline can classify minerals and subgrains into objects and obtain information about optic-axis orientation. Five metamorphic rocks were chosen to test and illustrate the method. Thin sections were imaged using reflected and cross- and plane-polarised transmitted light. Images were taken at different angles of the polariser and analyser (360° with 10° steps), both with and without the full-lambda plate. The resulting image stacks were analysed with a modular pipeline for optic-axis mapping (POAM). POAM consists of external and internal software packages that register, segment, classify, and interpret the visible light spectra using object-based image analysis (OBIAS). The mapped fields-of-view and grain orientation stereonets of interest are presented in the context of whole-slide images. Two innovations are reported. First, we used hierarchical tree region merging on blended multimodal images to classify individual grains of rock-forming minerals into objects. Second, we assembled a new optical mineralogy algorithm chain that identifies the mineral slow axis orientation. The c-axis orientation results were verified with scanning electron microscopy electron backscattered diffraction (SEM-EBSD) data. For quartz (uniaxial) in a granite mylonite the test yielded excellent correspondence of c-axis azimuth and good agreement for inclination. For orthorhombic orthopyroxene in a deformed garnet harzburgite, POAM produced acceptable results for slow axis azimuth. In addition, the method identified slight anisotropy in garnet that would not be appreciated by traditional microscopy. We propose that our method is ideally suited for two commonly performed tasks in mineralogy. First, for mineral grain classification of entire thin sections scans on blended images to provide automated modal abundance estimates and grain size distribution. Second, for prospective fields of view of interest, POAM can rapidly generate slow axis crystal orientation maps from multiangle image stacks on conventionally prepared thin sections for targeting detailed SEM-EBSD studies.

A generalized framework for agricultural field delineation from high-resolution satellite imageries

ABSTRACT Accurate digital data on agricultural fields are crucial for various agriculture applications. While deep learning methods have shown promise in delineating fields from high-resolution imagery, there is a lack of research evaluating key techniques for field boundary detection. Challenges also exist in converting detection results into high-quality fields. This study addresses these issues and proposes a generalized framework for agricultural field delineation (GF-AFD). First, we identify three key techniques for field boundary detection and apply them to MPSPNet model. Ablation and comparison experiments demonstrate significant performance enhancements due to techniques. They also prove effective for DeeplabV3+ model, which shares a similar architecture. The modified models outperform U-Net-based models and approach the state-of-the-art models. Second, we show that performing region segmentation on boundary results yields improved field shapes. To address weak boundary loss and unstable parameters issues in existing segmentation-based methods, we introduce the OWT method to enhance weak boundaries directionally before segmentation. We also develop a hierarchical merging method, leveraging the observational hierarchy of fields, resulting in stable parameters across regions and models. The proposed GF-AFD framework was validated cross three diverse Chinese counties. The results demonstrate the framework's robust performance, providing a valuable solution for delineating agricultural fields.

Sequencing-Enabled Hierarchical Cooperative CAV On-Ramp Merging Control With Enhanced Stability and Feasibility

Sequencing-Enabled Hierarchical Cooperative CAV On-Ramp Merging Control With Enhanced Stability and Feasibility

A Novel Image Segmentation Technique with N-L Means Filter Using Region Merging Strategy

Image segmentation is a branch of digital image processing which emphases on splitting an image into different fragments according to their features. This work proposes a novel image segmentation technique using fast non local (N-L) means filter with region merging approach. Here, a random noise is added to the original image and discrete wavelet transform (DWT) is applied to separate the low and high frequency bands of the digital image. The low frequency component is subjected to a fast N-L means filter and the filtered image is segmented by graph-based segmentation technique. Here, these segmented image regions are merged to reduce the over segmentation patterns. The detailed coefficients of the image component are treated with soft threshold filter to reduce the residual noise. Finally merged image is combined with preserved edge features at wavelet projection resulting in better segmented image. The results are compared with some of the best methods available in literature and are found to be significantly better. Keywords: Discrete wavelet transform (DWT), fast NL-means filter, graph-based segmentation, boundary based hierarchical region merging, soft-thresholding, peak signal to noise ratio (PSNR).

Analysis of Kozai cycles in equal-mass hierarchical triple supermassive black hole mergers in the presence of a stellar cluster

ABSTRACT Supermassive black holes (SMBHs) play an important role in galaxy evolution. Binary and triple SMBHs can form after galaxy mergers. A third SMBH may accelerate the SMBH merging process, possibly through the Kozai mechanism. We use N-body simulations to analyse oscillations in the orbital elements of hierarchical triple SMBHs with surrounding star clusters in galaxy centres. We find that SMBH triples spend only a small fraction of time in the hierarchical merger phase (i.e. a binary SMBH with a distant third SMBH perturber). Most of the time, the enclosed stellar mass within the orbits of the innermost or the outermost SMBH is comparable to the SMBH masses, indicating that the influence of the surrounding stellar population cannot be ignored. We search for Eccentric Kozai–Lidov (EKL) oscillations for which (i) the eccentricity of the inner binary and inclination are both oscillate and are antiphase or in-phase and (ii) the oscillation period is consistent with EKL time-scale. We find that EKL oscillations are short-lived and rare: the triple SMBH spends around 3 per cent of its time in this phase over the ensemble of simulations, reaching around 8 per cent in the best-case scenario. This suggests that the role of the EKL mechanism in accelerating the SMBH merger process may have been overestimated in previous studies. We follow-up with three-body simulations, using initial conditions extracted from the simulation, and the result can to some extent repeat the observed EKL-like oscillations. This comparison provides clues about why those EKL oscillations with perturbing stars are short-lived.

GROUND FILTERING OF CO-REGISTERED MOBILE AND STATIONARY LASER SCANS BY USING SUPERPOINTS IN RANSAC PLANES

Abstract. Ground filtering is an important tool for many applications. The high variability of landscapes makes it necessary to perform its computation with 3D points as the only input, that is, with as few as possible algorithm parameters and without any training data. In the case of terrestrial laser scans, an additional challenge comes from a highly inhomogeneous point density. The SiRP algorithm on ground filtering, relying on intermediate validating superpixels as ground or non-ground, was previously developed for airborne point clouds. We consider a dataset acquired by a mobile mapping system mounted on a car, which was extended by additional stationary laser scans. The registration algorithm is based on hierarchical merging. Afterwards, SiRP was applied to both uni-modal and multi-modal point clouds. Impressive qualitative and quantitative classification results with around 97 % on overall accuracy were obtained and discussed.

DARK MATTER HALOS IN NUMERICAL MODELS AT REDSHIFTS 0 ≤ <i>z</i> ≤ 9

For the numerical model in the range of redshifts \(0 \leqslant z \leqslant 9\), we examined the properties and evolution of dark matter haloes using a previously proposed method of compact analysis that allows separating the influence of random and regular factors on the main characteristics of the dark matter halo. In the investigated range of redshifts, a monotonic evolution of the average values of the basic parameters of small halo structures into a central massive object is observed through sequential hierarchical merging. These basic parameters include the circular velocity \( {{{v}}_{c}} \), the parameter \( {{w}_{c}} = {{{v}}_{c}}{\text{/}}r \), and the mass. In the range \(3 \leqslant z \leqslant 9\), the parameters evolve slowly, while in the range \(0 \leqslant z \leqslant 3\), they evolve rapidly. The evolution of the dark matter halos formed before reionization is characterized by a slow change in their average characteristics and the properties of the halo outskirts. The important role of early-formed massive structural elements is emphasized.

Chaotic Type I migration in turbulent discs

ABSTRACT By performing global hydrodynamical simulations of accretion discs with driven turbulence models, we demonstrate that elevated levels of turbulence induce highly stochastic migration torques on low-mass companions embedded in these discs. This scenario applies to planets migrating within gravito-turbulent regions of protoplanetary discs as well as stars and black holes embedded in the outskirts of active galactic nucleus (AGN) accretion discs. When the turbulence level is low, linear Lindblad torque persists in the background of stochastic forces and its accumulative effect can still dominate over relatively long time-scales. However, in the presence of very stronger turbulence, classical flow patterns around the companion embedded in the disc are disrupted, leading to significant deviations from the expectations of classical Type I migration theory over arbitrarily long time-scales. Our findings suggest that the stochastic nature of turbulent migration can prevent low-mass companions from monotonically settling into universal migration traps within the traditional laminar disc framework, thus reducing the frequency of three-body interactions and hierarchical mergers compared to previously expected. We propose a scaling for the transition mass ratio from classical to chaotic migration q ∝ αR, where αR is the Reynolds viscosity stress parameter, which can be further tested and refined by conducting extensive simulations over the relevant parameter space.