Merging Algorithm Research Articles

Overview and public data release of the augmented Auriga Project: cosmological simulations of dwarf and Milky Way-mass galaxies

ABSTRACT We present an extended suite of the Auriga cosmological gravo-magnetohydrodynamical ‘zoom-in’ simulations of 40 Milky Way-mass haloes and 26 dwarf galaxy–mass haloes run with the moving-mesh code arepo. Auriga adopts the Lambda cold dark matter cosmogony and includes a comprehensive galaxy formation physics model following the coupled cosmic evolution of dark matter, gas, stars, and supermassive black holes which has been shown to produce numerically well-converged galaxy properties for Milky Way-mass systems. We describe the first public data release of this augmented suite of Auriga simulations, which includes raw snapshots, group catalogues, merger trees, initial conditions, and supplementary data, as well as public analysis tools with worked examples of how to use the data. To demonstrate the value and robustness of the simulation predictions, we analyse a series of low-redshift global properties that compare well with many observed scaling relations, such as the Tully–Fisher relation, the star-forming (SF) main sequence, and H i gas fraction/disc thickness. Finally, we show that SF gas discs appear to build rotation and velocity dispersion rapidly for $z\gtrsim 3$ before they ‘settle’ into ever-increasing rotation-dispersion ratios ($V/\sigma$). This evolution appears to be in rough agreement with some kinematic measurements from H$\alpha$ observations, and demonstrates an application of how to utilize the released data.

Multinomial Logistic Regression with Adaptive Regularization for Cancer Subtype Classification via Multi-omics Data

Background: Integrating multi-omics data for cancer classification brings complementary biological insights while also facing challenges such as data integration, gene grouping, and adaptive weight construction. Objective: This paper aims to address the challenges faced by the cancer subtype classification and gene screening based on multi-omics data. Methods: Multinomial logistic regression with adaptive regularization (MLRAR) was proposed by integrating DNA methylation, gene mutation, and RNA-seq information. A data preprocessing strategy that effectively utilizes multi-omics information was presented, and the local maximum quasiclique merging (lmQCM) algorithm was implemented to group genes. Biological pathway information was utilized to evaluate the significance of gene groups, while the significance of each gene within a group was evaluated by integrating mutation information, information theory, and methylation information. Results: Compared to MRlasso, MRGL, MSGL, MROGL, AMRSOGL, and AGLRMR, the proposed method yielded improvements in subtype classification accuracy of breast cancer by 2.6%, 2.9%, 3.5%, 2.3%, 2.0%, and 1.8%, respectively. In addition, MLRAR also achieved significant improvements in ovarian cancer by 8.2%, 5.0%, 6.8%, 5.2%, 12.7%, and 6.3%, respectively. Conclusion: The proposed method can effectively deal with data integration, gene grouping, and adaptive weight construction.

Unveiling the (in)consistencies among the galaxy stellar mass function, star formation histories, satellite abundances, and intracluster light from a semi-empirical perspective

ABSTRACT In a hierarchical, dark matter-dominated Universe, stellar mass functions (SMFs), galaxy merger rates, star formation histories (SFHs), satellite abundances, and intracluster light (ICL), should all be intimately connected observables. However, the systematics affecting observations still prevent universal and uniform measurements of, for example, the SMF and the SFHs, inevitably preventing theoretical models to compare with multiple data sets robustly and simultaneously. We here present our holistic semi-empirical model decode (Discrete statistical sEmi-empiriCal mODEl) that converts via abundance matching dark matter merger trees into galaxy assembly histories, using different SMFs in input and predicting all other observables in output in a fully data-driven and self-consistent fashion with minimal assumptions. We find that: (1) weakly evolving or nearly constant SMFs below the knee ($M_\star \lesssim 10^{11} \, \mathrm{M}_\odot$) are the best suited to generate SFHs aligned with those inferred from MaNGA, SDSS, GAMA, and, more recently, JWST; (2) the evolution of satellites after infall only affects the satellite abundances and SFHs of massive central galaxies but not their merger histories; (3) the resulting SFR–$M_\star$ relation is lower in normalization by a factor of $\sim 2$ with respect to observations, with a flattening at high masses more pronounced in the presence of mergers; (4) the latest data on ICL can be reproduced if mass-loss from mergers is included in the models. Our findings are pivotal in acting as pathfinder to test the self-consistency of the high-quality data from, e.g. JWST and Euclid.

Diagnosing the massive-seed pathway to high-redshift black holes: statistics of the evolving black hole to host galaxy mass ratio

ABSTRACT Supermassive black holes (SMBHs) with masses of ∼109 M⊙ within the first billion year of the universe challenge our conventional understanding of black hole formation and growth. One pathway to these SMBHs proposes that supermassive stars born in pristine atomic cooling haloes yield massive seed BHs evolving to these early SMBHs. This scenario leads to an overly massive BH galaxy (OMBG), in which the BH to stellar mass ratio is initially Mbh/M* ≥ 1, well in excess of the typical values of ∼10−3 at low redshifts. Previously, we have investigated two massive seed BH candidates from the Renaissance simulation and found that they remain outliers on the Mbh–M* relation until the OMBG merges with a much more massive halo at z = 8. In this work, we use Monte-Carlo merger trees to investigate the evolution of the Mbh–M* relation for 50 000 protogalaxies hosting massive BH seeds, across 10 000 trees that merge into a 1012 M⊙ halo at z = 6. We find that up to 60 per cent (depending on growth parameters) of these OMBGs remain strong outliers for several 100 Myr, down to redshifts detectable with JWST and with sensitive X-ray telescopes. This represents a way to diagnose the massive-seed formation pathway for early SMBHs. We expect to find ∼0.1–1 of these objects per JWST Near Infrared Camera (NIRCam) field per unit redshift at z ≳ 6. Recently detected SMBHs with masses of ∼107 M⊙ and low-inferred stellar-mass hosts may be examples of this population.

Automatic reconstruction of radar pulse repetition pattern based on model learning

Compared with the traditional pulse repetition interval (PRI) parameters, PRI pattern can describe the temporal characteristics of radar more completely, and the method based on PRI pattern is more suitable for tasks of deinterleaving and identification. Therefore, effective reconstruction of PRI pattern is an important task in radar signal processing. However, previous PRI pattern reconstruction methods either set too many subjective parameters or have high requirements for the number of pulses, making it not very practical for analyzing actual radar signals. In this paper, the temporal rule of pulse train is modeled from the perspective of radar timing state switching. Then the pulses of different timing states in the pulse train are clustered based on state merging algorithms in model learning. The transitions between different timing states are established based on the pulse sets obtained by clustering. Finally, the PRI pattern of radar is reconstructed after processing interferential pulses and missing pulses. Based on the above method, this paper further proposes a method to reconstruct PRI patterns of all radars in interleaved pulse trains. Simulation results verify the effectiveness of the proposed PRI pattern reconstruction method.

Characterizing ultra-high-redshift dark matter halo demographics and assembly histories with the gureft simulations

ABSTRACT Dark matter halo demographics and assembly histories are a manifestation of cosmological structure formation and have profound implications for the formation and evolution of galaxies. In particular, merger trees provide fundamental input for several modelling techniques, such as semi-analytic models (SAMs), sub-halo abundance matching (SHAM), and decorated halo occupation distribution models. Motivated by the new ultra-high-redshift (z ≳ 10) frontier enabled by JWST, we present a new suite of Gadget at Ultrahigh Redshift with Extra-Fine Timesteps (gureft) dark matter-only cosmological simulations that are carefully designed to capture halo merger histories and structural properties in the ultra-z universe. The simulation suite consists of four 10243-particle simulations with box sizes of 5, 15, 35, and 90 Mpc h−1, each with 170 snapshots stored between 40 ≥ z ≥ 6. With the unprecedented number of available snapshots and strategically chosen dynamic range covered by these boxes, gureft uncovers the emerging dark matter halo populations and their assembly histories in the earliest epochs of cosmic history. In this work, we present the halo mass functions (HMF) between z ∼ 20 and 6 down to log (Mvir/M⊙) ∼ 5, and show that at high redshift, these robust HMFs can differ substantially from commonly used analytic approximations or older fitting functions in the literature. We also present key physical properties of the ultra-high z halo population, such as concentration and spin, as well as their mass growth and merger rates, and again provide updated fitting functions.

Predicting Stellar Masses of the First Galaxies Using Graph Neural Networks

Theoretical models of galaxy formation and evolution are primarily investigated through cosmological simulations and semi-analytical models. The former method consumes O(106) core-hours explicitly modeling the dynamics of the galaxies, whereas the latter method only requires O(103) core-hours foregoing directly simulating internal structure for computational efficiency. In this work, we present a proof-of-concept machine learning regression model, using a graph neural network architecture, to predict the stellar mass of high-redshift galaxies solely from their dark matter merger trees, trained from a radiation hydrodynamics cosmological simulation of the first galaxies.

Forecasting solar power generation using evolutionary mating algorithm-deep neural networks

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.

Automatic Marker Generation to Similarity Based Region Merging Algorithm using Edge Information

One of the most important tasks in computer vision is image segmentation. Several interactive strategies are utilized for picture segmentation because automated techniques are difficult for this kind of work. The outcome of interactive methods is mostly determined by user input. Obtaining excellent interactions for huge datasets is challenging. However, automated picture segmentation is starting to play a significant role in image analysis and computer vision. Effective and efficient segmentation outcomes are obtained using the interacting region merging method suggested by Maximal Similarity based region merging algorithm. Limitation of MSBRM is it requires some efforts on the part of users and is yet not a fully-automatic approach. Here, we suggest a completely fresh unsupervised image segmentation method that combines edge information with MSBRM. We propose an integrated framework to generate object markers for similarity based region algorithm using edge information. Long edges give rough distribution of objects in image. After retrieving edges using phase congruency, edge processing operations are employed to remove small edges and to group color similar long boundaries. Centroids of long boundaries are used as object markers to the MSBRM algorithm. The generation of object markers is done using edge segment grouping. These object markers guide the region merging process. The proposed method shows its effectiveness in segmenting natural real world color images.

Leaving No Branches Behind: Predicting Baryonic Properties of Galaxies from Merger Trees

Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here, we present a graph-neural-network-based model that is able to accurately predict key properties of galaxies such as stellar mass, g − r color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range (z = 0–5), for all galaxies with stellar masses more massive than 109 M ⊙ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model.

Merger-tree-based Galaxy Matching: A Comparative Study across Different Resolutions

We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of resolution biases through the galaxy-by-galaxy inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of IllustrisTNG simulations to assess the impact on galaxy properties. We find that, with the subgrid physics model calibrated for TNG100-1, subhalos in TNG100-1 (high resolution) have ≲0.5 dex higher stellar masses than their counterparts in the TNG100-2 (low resolution). It is also discovered that the subhalos with M gas ∼ 108.5 M ⊙ in TNG100-1 have ∼0.5 dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of subhalos in TNG100-2 converge well with those from TNG100-1, except within 4 kpc of the resolution limit. The differences in stellar mass and hot gas mass are most pronounced in the central region. We exploit machine learning to build a correction mapping for the physical quantities of subhalos from low- to high-resolution simulations (TNG300-1 and TNG100-1), which enables us to find an efficient way to compile a high-resolution galaxy catalog even from a low-resolution simulation. Our tools can easily be applied to other large cosmological simulations, testing and mitigating the resolution biases of their numerical codes and subgrid physics models.

Deep reinforcement learning algorithm based ramp merging decision model

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. This paper designs a ramp merging decision model based on deep deterministic policy gradient algorithm (DDPG) to solve the vehicle merging problem. To address the problems of slow algorithm merging and poor robustness of previous deep reinforcement learning algorithms in the field of intelligent vehicle ramp merging leading to the low success rate of intelligent vehicle merging, first, we introduce a simple recurrent unit (SRU) for extracting intelligent vehicle states and environment features and use the DDPG algorithm for intelligent vehicle decision making. Second, the experience playback pool of DDPG algorithm is improved by using priority sampling instead of uniform sampling. Finally, a multi-objective reward function is set up during training, considering factors such as safety and efficiency. The simulation experiments show that the improved algorithm improves the merging speed of the model, reduces the collision rate, and enables the vehicle to make more reasonable decisions. In addition, the superiority of the method is demonstrated by comparing with the advanced method.

Super-Eddington accretion in high-redshift black holes and the emergence of jetted AGN

Abstract In this work we study the co-evolution of central black holes (BHs) and host galaxies by utilizing an advanced iteration of the DELPHI semi-analytical model of galaxy formation and evolution. Based on dark matter halo merger trees spanning the redshift range from z = 20 to z = 4, it now incorporates essential components such as gas heating and cooling, cold and hot BH accretion, jet and radiative AGN feedback. We show how different BH growth models impact quasar and galaxy observables at z ≥ 5, providing predictions that will help discriminate between super-Eddington and Eddington-limited accretion models: despite being both consistent with observed properties of SMBHs and their host galaxies at z ∼ 5 − 7, they become very clearly distinguishable at higher redshift and in the intermediate mass regime. We find that the super-Eddington model, unlike the Eddington-limited scenario, predicts a gap in the BH mass function corresponding to the intermediate-mass range 104 M⊙ < Mbh < 106 M⊙. Additionally, it predicts black holes up to two orders of magnitude more massive for the same stellar mass at z = 9. The resulting velocity dispersion – BH mass relation at z ≥ 5 is consistent with local measurements, suggesting that its slope and normalisation are independent of redshift. Depending on the Eddington ratio, we also model the emergence of AGN jets, predicting their duty cycle across as a function of BH mass and their potential impact on the observed number density distribution of high-redshift AGN in the hard X-ray band.

ELSM: Evidence-Based Line Segment Merging

Abstract Existing line segment detectors break perceptually contiguous linear structures into multiple line segments. This can be offset by re-merging the segments, but existing merging algorithms over-merge and produce globally incorrect segments. Geometric cues are necessary but not sufficient for deciding whether to merge two segments or not. By restricting the result of any merging decision to have underlying image support, we reduce over-merging and globally incorrect segments. We propose a novel measure for evaluating merged segments based on line segment Hausdorff distance. On images from YorkUrbanDB, we show that our algorithm improves both qualitative and quantitative results obtained from four existing line segment detection methods and is better than two existing line segment merging methods. Our method does not suffer from inconsistent results produced by four recent deep learning-based models. The method is easily customisable to work for line drawings such as hand-drawn maps to obtain vectorized representations.

Representing low-mass black hole seeds in cosmological simulations: A new sub-grid stochastic seed model

ABSTRACT The physical origin of the seeds of supermassive black holes (SMBHs), with postulated initial masses ranging from ∼105 M⊙ to as low as ∼102 M⊙, is currently unknown. Most existing cosmological hydrodynamic simulations adopt very simple, ad hoc prescriptions for BH seeding and seed at unphysically high masses ∼105–106 M⊙. In this work, we introduce a novel sub-grid BH seeding model for cosmological simulations that is directly calibrated to high-resolution zoom simulations that explicitly resolve ∼103 M⊙ seeds forming within haloes with pristine, dense gas. We trace the BH growth along galaxy merger trees until their descendants reach masses of ∼104 or 105 M⊙. The results are used to build a new stochastic seeding model that directly seeds these descendants in lower resolution versions of our zoom region. Remarkably, we find that by seeding the descendants simply based on total galaxy mass, redshift and an environmental richness parameter, we can reproduce the results of the detailed gas-based seeding model. The baryonic properties of the host galaxies are well reproduced by the mass-based seeding criterion. The redshift-dependence of the mass-based criterion captures the combined influence of halo growth, dense gas formation, and metal enrichment on the formation of ∼103 M⊙ seeds. The environment-based seeding criterion seeds the descendants in rich environments with higher numbers of neighbouring galaxies. This accounts for the impact of unresolved merger dominated growth of BHs, which produces faster growth of descendants in richer environments with more extensive BH merger history. Our new seed model will be useful for representing a variety of low-mass seeding channels within next-generation larger volume uniform cosmological simulations.

Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion

Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion

The effect of pressure-anisotropy-driven kinetic instabilities on magnetic field amplification in galaxy clusters

Context. The intracluster medium (ICM) is the low-density diffuse gas that fills the space between galaxies within galaxy clusters. It is primarily composed of magnetized plasma, which reaches virial temperatures of up to 108 K, probably due to mergers of subhalos. Under these conditions, the plasma is weakly collisional and therefore has an anisotropic pressure tensor with respect to the local direction of the magnetic field. This triggers very fast, Larmor-scale, pressure-anisotropy-driven kinetic instabilities that alter magnetic field amplification. Aims. We aim to study magnetic field amplification through a turbulent, small-scale dynamo, including the effects of the kinetic instabilities, during the evolution of a typical massive galaxy cluster. A specific aim of this work is to establish a redshift limit from which a dynamo has to start to amplify the magnetic field up to equipartition with the turbulent velocity field at redshift z = 0. Methods. We implemented one-dimensional radial profiles for various plasma quantities for merger trees generated with the modified GALFORM algorithm. We assumed that turbulence is driven by successive mergers of dark matter halos and constructed effective models for the Reynolds number Reeff dependence on the magnetic field in three different magnetization regimes (unmagnetized, magnetized “kinetic”, and magnetized “fluid”), including the effects of kinetic instabilities. The magnetic field growth rate is calculated for the different Reeff models. Results. The model results in a higher magnetic field growth rate at higher redshift. For all scenarios considered in this study, to reach equipartition at z = 0, it is sufficient for the amplification of the magnetic field to start at redshift zstart ≈ 1.5 and above. The time to reach equipartition can be significantly shorter in cases with systematically smaller turbulent forcing scales and for the highest Reeff models. Conclusions. The origin of magnetic fields in the weakly collisional ICM can be explained by the small-scale turbulent dynamo, provided that the dynamo process starts beyond a given redshift. Merger trees are useful tools for studying the evolution of magnetic fields in weakly collisional plasmas, and could also be used to constrain the different stages of the dynamo that could potentially be observed by future radio telescopes.

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

Air conditioning compressor crankshaft roundness measurement based on a laser profiler

Crankshafts are key components of air conditioning compressors. The good roundness of a crankshaft has positive effects on the operating stability, service life, working efficiency, and noise radiation of the compressor. This study proposes a line point cloud data merging method for measuring crankshaft roundness using a laser profiler. First, a crankshaft roundness measurement system for air conditioning compressors is developed, and a high-accuracy 2D laser displacement sensor is employed to collect the line point cloud data. Second, the steps of the merging method are divided into two parts: (1) the radial merging to eliminate the effects of eccentricity; (2) the circumferential merging to remove overlapping data points of two adjacent profiles. Then, a pin gauge is used to select the point cloud region of the profile line for data merging. Experimental results show that the measurement repeatability of the proposed system can be controlled at the micrometer level. Besides, the roundness measured from the merged data is very close to the roundness measuring instrument measurements, which verifies the accuracy of the measurement system and the validity of the line point data merging algorithm.

Predicting discharge coefficient of weir–orifice in closed conduit using a neuro-fuzzy model improved by multi-phase PSOGSA

This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.