arXiv subjects

Yu Dai

Publications and source records attributed to Yu Dai.

At least 19 recordsLinked to original sources

arXiv2026

JAMER: Project-Level Code Framework Dataset and Benchmark on Professional Game Engines

Current AI-driven game development has made substantial progress in asset generation, gameplay design, and web-based game coding, yet project-level code engineering on professional game engines remains largely unexplored due to the absence of large-scale datasets and deterministic evaluation methods. We present JamSet and JamBench, the first project-level game code framework dataset and benchmark built on a professional game engine. Our key insight is that Game Jam competitions, community events where developers build complete games under tight time constraints, yield thousands of open-source projects suitable for this purpose. Building on the Godot engine's text-based format and headless execution mode, we design a deterministic verification pipeline from file integrity to runtime behavior collection, distilling 8,133 verified projects from over 240,000 repositories. Of these, 300 manually verified projects form JamBench; the rest constitute JamSet. JamBench defines theme-driven generation and code completion tasks, evaluated through a pipeline combining compilation pass rates, Structural Completeness Score (SCS), and Behavioral Alignment Score (BAS). Evaluation of 9 frontier models reveals a capability cliff as project scale increases, with runtime pass rates dropping from 80.4% on small projects to 5.7% on large ones (Task2a). Code Agents improve compilation rates yet yield no gains in runtime behavioral quality, indicating that the bottleneck lies in architectural design rather than syntactic correctness. Experiments validate JamSet as effective training data. All data and code are publicly available.

cs.SE↗

arXiv2026

Missing pairs in open cluster catalogs

Open clusters (OCs) in our Galaxy can be found in pairs, possibly forming physical binaries, or in groups. These objects offer unique insights into the process of star formation and testify to the dynamical interactions at local and galactic scales. Therefore, building as complete a census as possible is a valuable endeavor. This work is aimed at identifying and characterizing new OC pair candidates that had been overlooked in previous studies. Two recent comprehensive catalogs were cross-matched to identify OCs in the first catalog that had been missing from the second one. From this list, counterparts in the second catalog were searched within a 3D distance of 50 pc. Candidate pairs were then selected by applying constraints on the tangential velocity (TV) difference. An orbital integration was performed to assess gravitational binding. The similarity in terms of the radial velocity (RV) and age was evaluated. We identified seven isolated binary cluster candidates, comprising two likely bound systems with stable orbits over 100 Myr; two pairs with a possible common origin but lacking RV confirmation; and three pairs with significant velocity discrepancies, suggesting they are unbound or in transitional states. We also identified six cluster group candidates, while refining the membership of known complexes such as UBC\_672 and NGC\_1977, and discovering a new group around FSR\_0198. Notably, the UBC\_392 group exhibits coherent proper motions but inconsistent RVs and large age spreads, indicating that it is not gravitationally bound. Additionally, we reconciled 15 clusters with discrepant nomenclature between the two catalogs. Multi-catalog integration combined with kinematic and dynamical validation is essential for establishing a complete census of Galactic cluster pairs.

astro-ph.GA↗

arXiv2026

Multiple Extreme Ultraviolet Peaks Attributed to Three-dimensional Magnetic Reconnection in a Long-duration Solar Flare

Solar flares are a major driver of hazardous space weather, whose intense electromagnetic emissions and energetic particles can significantly disturb the near-Earth space environment. Therefore, understanding the physical processes during a solar flare and predicting its radiation profiles are of great importance. In this study, we analyze and model an M1.4 two-ribbon long-duration flare, whose multiple extreme-ultraviolet (EUV) emission peaks are found to correspond to different three-dimensional (3D) magnetic reconnections driven by the continuous evolution of a flux rope. In particular, the second and third peaks in the 335 {\AA} EUV channel originate from longer and higher flare loops with extended cooling times, formed by reconnection between flux-rope field lines and ambient sheared-arcade field lines ($ar\text{--}rf$) and between flux-rope field lines themselves ($rr\text{--}rf$). These results are supported by the drifting of the flux-rope footpoint (and flare ribbon) and the decrease in toroidal flux of the flux rope, as well as by the connectivity transfer of representative field lines in the magnetohydrodynamic (MHD) simulation. This work points out, for the first time, new manifestations of the 3D flare scenario in EUV light curves. On the one hand, it provides an explanation for two-ribbon late-phase flares. On the other hand, the conclusions presented here help bridge the gap between imaging observations, EUV light-curve diagnostics, and the magnetic structures of the associated coronal mass ejections.

astro-ph.SR↗

arXiv2026

Influence of Cross-sectional Expansion on Coronal Emissions from a Radiatively Cooling Solar Flare Loop

Loop-aligned hydrodynamic modelings help better understand the thermodynamic evolution of flaring plasma confined in solar flare loops. Conventional loop modelings typically assume a uniform loop cross section. With a variation of the cross section taken into account, in this work we carry out both analytical and numerical modelings of the radiative cooling in a solar flare loop. It is found that a cross-sectional expansion with height can efficiently suppress the draining of loop material from the corona while not significantly affecting the decrease of loop temperature. Reflected to the loop energetics, the coronal part of the loop cools more dominantly by radiation, and more importantly, the loop radiative outputs are shifted toward lower temperatures. These findings pose important physical implications for extreme-ultraviolet (EUV) late-phase emissions discovered in some solar flares. The late-phase loops in these flares are believed to bear a more notable cross-sectional expansion owing to their longer lengths. Compared with the main-phase loops, the late-phase loops would emit more effectively at middle temperatures, which could, to a certain degree, mitigate the severe heating requirement for the production of a prominent warm coronal late-phase peak. In addition, the cross-sectional expansion also affects the shape of the emission lights curves, causing a sharper decay after the emission peak. Such an emission pattern has been validated with the observations of an EUV late-phase flare, and could serve as a potential diagnostic tool to judge the degree of loop cross-sectional expansion in an extended flare dataset.

astro-ph.SR↗

arXiv2026

The morphological stability of open clusters: a new 2D perspective

Open clusters (OCs) usually evolve gradually as the number of their members changes, which can be manifested in their morphological characteristics. We aim to investigate the morphological stability of 1,490 OCs and further explore the potential change of morphological stability of the OCs at different spatial positions, using the OC catalog from the literature. We define for the first time a new morphological stability parameter Ncore/Nouter, a ratio of member numbers between cluster core and outer areas within tidal radii, which has a significant positive correlation against N, with a slope of 1.140$\pm$0.039, significantly steeper than the 0.720$\pm$0.026 measured for Score/Souter. This demonstrates that the stellar density in the core is a more sensitive tracer for morphological stability than geometry. Spatially, the radial sample OCs have larger slopes of Ncore/Nouter and Score/Souter against N, with 1.083$\pm$0.116 and 0.733$\pm$0.080, respectively, whereas those in the tangential direction 1.013$\pm$0.110 and 0.529$\pm$0.075, respectively, which means that the impact on sample OCs from tidal forces directed toward the Galactic center is possibly stronger than that from the shear force caused by the differential rotation of the Galactic disk. Besides, the sample OCs younger than 30 Myr display a shallow slope of 0.751$\pm$0.166, with those older than 800 Myr (1.442$\pm$0.128), reflecting that young OCs likely endure both internal disruptions, such as early dynamical heating weakening core binding and more severe external disturbances, compared to older OCs.

astro-ph.GA↗

arXiv2026

World Craft: Agentic Framework to Create Visualizable Worlds via Text

Large Language Models (LLMs) motivate generative agent simulation (e.g., AI Town) to create a ``dynamic world'', holding immense value across entertainment and research. However, for non-experts, especially those without programming skills, it isn't easy to customize a visualizable environment by themselves. In this paper, we introduce World Craft, an agentic world creation framework to create an executable and visualizable AI Town via user textual descriptions. It consists of two main modules, World Scaffold and World Guild. World Scaffold is a structured and concise standardization to develop interactive game scenes, serving as an efficient scaffolding for LLMs to customize an executable AI Town-like environment. World Guild is a multi-agent framework to progressively analyze users' intents from rough descriptions, and synthesizes required structured contents (\eg environment layout and assets) for World Scaffold . Moreover, we construct a high-quality error-correction dataset via reverse engineering to enhance spatial knowledge and improve the stability and controllability of layout generation, while reporting multi-dimensional evaluation metrics for further analysis. Extensive experiments demonstrate that our framework significantly outperforms existing commercial code agents (Cursor and Antigravity) and LLMs (Qwen3 and Gemini-3-Pro). in scene construction and narrative intent conveyance, providing a scalable solution for the democratization of environment creation.

cs.HC↗

arXiv2025

Ferromagnetic interface engineering of spin-charge conversion in RuO$_2$

Spin-orbit torque efficiency is conventionally fixed by bulk materials. $D$-wave altermagnets introduce an additional nonrelativistic spin-charge conversion channel beyond inverse spin-Hall effect. Using prototypical candidate RuO$_2$ as an example, we show that the adjacent ferromagnet alone can dictate both the magnitude and sign of spin-charge conversion. Spin-pumping measurements on RuO$_2$/Y$_3$Fe$_5$O$_{12}$ (YIG) and RuO$_2$/Ni$_{80}$Fe$_{20}$ (Py) bilayers yield opposite effective spin-Hall angles that persist across crystalline and polycrystalline RuO$_2$. Inserting an ultrathin Au spacer at the RuO$_2$/YIG interface reverses the signal, envidencing a dominant interfacial inverse Rashba-Edelstein effect, whereas RuO$_2$/Py is governed by bulk inverse spin-Hall effect. First-principles calculations trace this dichotomy to interface-selective band hybridization: Rashba surface states survive at the insulating YIG contact yet are quenched by metallic Py. Our findings establish ferromagnetic interfacing as a deterministic knob for tailoring spin-charge conversion in altermagnetic oxides, paving the way to field-free, low-dissipation spintronic memory devices.

cond-mat.mtrl-sci↗

arXiv2025

Closing the Expression Gap in LLM Instructions via Socratic Questioning

A fundamental bottleneck in human-AI collaboration is the ``intention expression gap," the difficulty for humans to effectively convey complex, high-dimensional thoughts to AI. This challenge often traps users in inefficient trial-and-error loops and is exacerbated by the diverse expertise levels of users. We reframe this problem from passive instruction following to a Socratic collaboration paradigm, proposing an agent that actively probes for information to resolve its uncertainty about user intent. we name the proposed agent Nous, trained to acquire proficiency in this inquiry policy. The core mechanism of Nous is a training framework grounded in the first principles of information theory. Within this framework, we define the information gain from dialogue as an intrinsic reward signal, which is fundamentally equivalent to the reduction of Shannon entropy over a structured task space. This reward design enables us to avoid reliance on costly human preference annotations or external reward models. To validate our framework, we develop an automated simulation pipeline to generate a large-scale, preference-based dataset for the challenging task of scientific diagram generation. Comprehensive experiments, including ablations, subjective and objective evaluations, and tests across user expertise levels, demonstrate the effectiveness of our proposed framework. Nous achieves leading efficiency and output quality, while remaining robust to varying user expertise. In conclusion, our research provides a systematic methodology and a new perspective for addressing the issue of ambiguous intentions in complex human-machine collaboration.

cs.AI↗

arXiv2025

From Pixels to Paths: A Multi-Agent Framework for Editable Scientific Illustration

Scientific illustrations demand both high information density and post-editability. However, current generative models have two major limitations: Frist, image generation models output rasterized images lacking semantic structure, making it impossible to access, edit, or rearrange independent visual components in the images. Second, code-based generation methods (TikZ or SVG), although providing element-level control, force users into the cumbersome cycle of "writing-compiling-reviewing" and lack the intuitiveness of manipulation. Neither of these two approaches can well meet the needs for efficiency, intuitiveness, and iterative modification in scientific creation. To bridge this gap, we introduce VisPainter, a multi-agent framework for scientific illustration built upon the model context protocol. VisPainter orchestrates three specialized modules-a Manager, a Designer, and a Toolbox-to collaboratively produce diagrams compatible with standard vector graphics software. This modular, role-based design allows each element to be explicitly represented and manipulated, enabling true element-level control and any element can be added and modified later. To systematically evaluate the quality of scientific illustrations, we introduce VisBench, a benchmark with seven-dimensional evaluation metrics. It assesses high-information-density scientific illustrations from four aspects: content, layout, visual perception, and interaction cost. To this end, we conducted extensive ablation experiments to verify the rationality of our architecture and the reliability of our evaluation methods. Finally, we evaluated various vision-language models, presenting fair and credible model rankings along with detailed comparisons of their respective capabilities. Additionally, we isolated and quantified the impacts of role division, step control,and description on the quality of illustrations.

cs.CV↗

arXiv2025

High-Resolution Modelling of Coronae and Winds in Solar-type Stars with Varying Rotation Rates I. X-ray Coronae

Stellar coronae are believed to be the main birthplace of various stellar magnetic activities. However, the structures and properties of stellar coronae remain poorly understood. Using the Space Weather Modelling Framework with the Alfv\'{e}n Wave Solar Model (SWMF-AWSoM) and dynamo-generated surface magnetic maps, here we model the coronae of four solar-type stars. By incorporating the Sun, our work covers a range of stars with the rotation varying from 1.0 to 23.3 $\Omega_\odot$ (periods of 25 to 1 days). Guided by observations, we scale the magnetic field strength with increasing rotation, covering a range between 6.0 G to 1200 G approximately. In our models, energy release associated with small-scale magnetic flux is a key source of coronal heating and is essential for reproducing realistic coronal structures. Our models capture dense (1$-$2 orders of magnitude higher than solar values) and ultra-hot ($\sim 10\,\mathrm{MK}$) coronae dominated by closed field structures. Using the CHIANTI atomic database, we also compute synthetic X-ray spectra and derive the corresponding X-ray luminosities $(L_X)$, which follow a scaling law to magnetic field $L_X \propto \langle|\mathbf{B}|\rangle^{1.75}$. Furthermore, the coronal X-ray emission is found to be rotationally modulated by the alternating presence of bright active regions and dark coronal holes. These results provide new insights into the extremely high-energy coronae of rapidly rotating solar-type stars, which differ markedly from the Sun.

astro-ph.SR↗

arXiv2025

Noema formIng Cluster survEy (NICE): A Census of Star Formation and Cold Gas Properties in Massive protoclusters at 1.5<z<4

Massive protoclusters at z~1.5-4, the peak of the cosmic star formation history, are key to understanding the formation mechanisms of massive galaxies in today's clusters. However, studies of protoclusters at these high redshifts remain limited, primarily due to small sample sizes and heterogeneous selection criteria. In this work, we conduct a systematic investigation of the star formation and cold gas properties of member galaxies of eight massive protoclusters in the COSMOS field, using the statistical and homogeneously selected sample from the Noema formIng Cluster survEy (NICE). Our analysis reveals a steep increase in the star formation rates per halo mass ($\Sigma_{\rm SFR} /M_{\rm halo}$) with redshifts in these intensively star-forming protoclusters, reaching values one to two orders of magnitude higher than those observed in the field at z>2. We further show that, instead of an enhancement of starbursts, this increase is largely driven by the concentration of massive and gas-rich star-forming galaxies in the protocluster cores. The member galaxies still generally follow the same star formation main sequence as in the field, with a moderate enhancement at the low mass end. Notably, the most massive protocluster galaxies ($M_\star$>8$\times$10$^{10}$M$_\odot$) exhibit higher $f_{\rm gas}$ and $\tau_{\rm gas}$ than their field counterparts, while remaining on the star forming main sequence. These gas-rich, massive, and star-forming galaxies are predominantly concentrated in the protocluster cores and are likely progenitors of massive ellipticals in the center of today's clusters. These results suggest that the formation of massive galaxies in such environments is sustained by substantial gas reservoirs, which support persistent star formation and drive early mass assembly in forming cluster cores.

astro-ph.GA↗

arXiv2025

Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection

Few-shot object detection (FSOD) aims to detect objects with limited samples for novel classes, while relying on abundant data for base classes. Existing FSOD approaches, predominantly built on the Faster R-CNN detector, entangle objectness recognition and foreground classification within shared feature spaces. This paradigm inherently establishes class-specific objectness criteria and suffers from unrepresentative novel class samples. To resolve this limitation, we propose a Uniform Orthogonal Feature Space (UOFS) optimization framework. First, UOFS decouples the feature space into two orthogonal components, where magnitude encodes objectness and angle encodes classification. This decoupling enables transferring class-agnostic objectness knowledge from base classes to novel classes. Moreover, implementing the disentanglement requires careful attention to two challenges: (1) Base set images contain unlabeled foreground instances, causing confusion between potential novel class instances and backgrounds. (2) Angular optimization depends exclusively on base class foreground instances, inducing overfitting of angular distributions to base classes. To address these challenges, we propose a Hybrid Background Optimization (HBO) strategy: (1) Constructing a pure background base set by removing unlabeled instances in original images to provide unbiased magnitude-based objectness supervision. (2) Incorporating unlabeled foreground instances in the original base set into angular optimization to enhance distribution uniformity. Additionally, we propose a Spatial-wise Attention Disentanglement and Association (SADA) module to address task conflicts between class-agnostic and class-specific tasks. Experiments demonstrate that our method significantly outperforms existing approaches based on entangled feature spaces.

cs.CV↗

arXiv2025

A High-Quality Dataset and Reliable Evaluation for Interleaved Image-Text Generation

Recent advancements in Large Multimodal Models (LMMs) have significantly improved multimodal understanding and generation. However, these models still struggle to generate tightly interleaved image-text outputs, primarily due to the limited scale, quality, and instructional richness of current training datasets. To address this, we introduce InterSyn, a dataset that features: (1) large scale, comprising 1.8M multimodal samples; (2) high quality, supported by our proposed Self-Evaluation with Iterative Refinement (SEIR) method for rigorous automated quality refinement; (3) rich instructional diversity, ensured through diverse well-designed question templates, based on human preferences and covering a 3500-topic hierarchy. These characteristics make InterSyn particularly well-suited for training LMMs in interactive image-text generation capabilities. To evaluate the capabilities, we propose SynJudge, a reliable automatic evaluator that aligns closely with human judge and outputs four interpretable scores: Text Content Completeness (TCC), Image Content Completeness (ICC), Image Quality (IQ), and Image-Text Synergy (ITS). These scores are complementary, covering both content and quality as well as cross-modal interaction, thereby forming a comprehensive evaluation framework. Experimental results on InterSyn subsets of up to 200K samples show that 25K-50K already yield substantial improvements, while scaling to 100K/200K brings further gains in TCC, ICC, and especially ITS, highlighting InterSyn's: (1) scalability, as performance consistently improves with more data; (2) efficiency, as significant gains are achievable even with smaller subsets, making it accessible to researchers with varying computational resources.

cs.CV↗

arXiv2025

ARMOR: Empowering Multimodal Understanding Model with Interleaved Multimodal Generation Capability

Unified multimodal understanding and generation have recently received much attention in the area of vision and language. Existing UniMs are designed to simultaneously learn both multimodal understanding and generation capabilities, demanding substantial computational resources, and often struggle to generate interleaved text-image. We present ARMOR, a resource-efficient and pure autoregressive framework that achieves both understanding and generation by fine-tuning existing multimodal large language models (MLLMs). Specifically, ARMOR extends existing MLLMs from three perspectives: (1) For model architecture, an asymmetric encoder-decoder architecture with a forward-switching mechanism is introduced to unify embedding space integrating textual and visual modalities for enabling natural text-image interleaved generation with minimal computational overhead. (2) For training data, a meticulously curated, high-quality interleaved dataset is collected for fine-tuning MLLMs. (3) For the training algorithm, we propose a ``what or how to generate'' algorithm to empower existing MLLMs with multimodal generation capabilities while preserving their multimodal understanding capabilities, through three progressive training stages based on the collected dataset. Experimental results demonstrate that ARMOR upgrades existing MLLMs to UniMs with promising image generation capabilities, using limited training resources. Our code will be released soon at https://github.com/finyorko/armor.

cs.CV↗

arXiv2025

Modulating CNN Features with Pre-Trained ViT Representations for Open-Vocabulary Object Detection

Owing to large-scale image-text contrastive training, pre-trained vision language model (VLM) like CLIP shows superior open-vocabulary recognition ability. Most existing open-vocabulary object detectors attempt to utilize the pre-trained VLMs to attain generalized representation. F-ViT uses the pre-trained visual encoder as the backbone network and freezes it during training. However, its frozen backbone doesn't benefit from the labeled data to strengthen the representation for detection. Therefore, we propose a novel two-branch backbone network, named as \textbf{V}iT-Feature-\textbf{M}odulated Multi-Scale \textbf{C}onvolutional Network (VMCNet), which consists of a trainable convolutional branch, a frozen pre-trained ViT branch and a VMC module. The trainable CNN branch could be optimized with labeled data while the frozen pre-trained ViT branch could keep the representation ability derived from large-scale pre-training. Then, the proposed VMC module could modulate the multi-scale CNN features with the representations from ViT branch. With this proposed mixed structure, the detector is more likely to discover objects of novel categories. Evaluated on two popular benchmarks, our method boosts the detection performance on novel category and outperforms state-of-the-art methods. On OV-COCO, the proposed method achieves 44.3 AP$_{50}^{\mathrm{novel}}$ with ViT-B/16 and 48.5 AP$_{50}^{\mathrm{novel}}$ with ViT-L/14. On OV-LVIS, VMCNet with ViT-B/16 and ViT-L/14 reaches 27.8 and 38.4 mAP$_{r}$.

cs.CV↗

arXiv2024

Enhanced Peak and Extended Cooling of the Extreme-ultraviolet Late Phase in a Confined Solar Flare

We present observations and analysis of an X1.8 non-eruptive solar flare on 2012 October 23, which is characterized by an extremely large late-phase peak seen in the warm coronal extreme-ultraviolet (EUV) emissions ($\sim$ 3 MK), with the peak intensity over 1.4 times that of main flare peak. The flare is driven by a failed eruption of a magnetic flux rope (MFR), whose strong squeeze force acting on the overlying magnetic structures gives rise to an intense early heating of the late-phase loops. Based on differential emission measure (DEM) analysis, it is found that the late-phase loops experience a "longer-than-expected" cooling without the presence of any obvious additional heating, and meanwhile, their volume emission measure (EM) maintains a plateau level for a long time before turning into an evident decay. Without the need for an additional heating, we propose that the special thermodynamic evolution of the late-phase loops revealed in this flare might arise from loop cross-sectional expansions with height, which are evidenced by both direct measurements from EUV images and by magnetic field extrapolation. By blocking the losses of both heat flux and mass from the corona, such an upward cross-sectional expansion not only elongates the loop cooling time, but also more effectively sustains the loop density, therefore leading to a later-than-expected occurrence of the warm coronal late phase in combination with a sufficiently high late-phase peak. We further verify such a scenario by analytically solving the cooling process of a late-phase loop characterized by a variable cross section.

astro-ph.SR↗

arXiv2024

PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.

eess.IV↗

arXiv2024

NOEMA formIng Cluster survEy (NICE): Characterizing eight massive galaxy groups at $1.5 < z < 4$ in the COSMOS field

The NOEMA formIng Cluster survEy (NICE) is a large program targeting 69 massive galaxy group candidates at $z>2$ in six deep fields. We report spectroscopic confirmation of eight groups at $1.65\leq z\leq3.61$ in COSMOS. Homogeneously selected as significant overdensities of red IRAC sources with red Herschel colors, four groups are confirmed by CO and [CI] with NOEMA 3mm observations, three are confirmed with ALMA, and one is confirmed by H$\alpha$ from Subaru/FMOS. We constructed the integrated FIR SEDs for the eight groups, obtaining total IR SFR $=260-1300~{\rm M_\odot}$~yr$^{-1}$. We adopted six methods to estimate the dark matter masses, including stellar mass to halo mass relations, overdensity with galaxy bias, and NFW profile fitting to radial stellar mass density. We found the radial stellar mass density are consistent with a NFW profile, supporting that they are collapsed structures hosted by a single dark matter halo. The best halo mass estimates are $\log(M_{\rm h}/{\rm M_\odot})=12.8-13.7$ with uncertainty of 0.3 dex. From halo mass estimates, we derive baryonic accretion rate ${\rm BAR}=(1-8)\times10^{3}\,{\rm M_{\odot}/yr}$ for this sample. We find a quasi-linear correlation between the integrated SFR/BAR and the theoretical halo mass limit for cold streams, $M_{\rm stream}/M_{\rm h}$, with ${\rm SFR/BAR}=10^{-0.46\pm0.22}\left({M_{\rm stream}/M_{\rm h}}\right)^{0.71\pm0.16}$ with a scatter of $0.40\,{\rm dex}$. Further, we compare halo masses and stellar masses with simulations, and find all structures are consistent with being progenitors of $M_{\rm h}(z=0)>10^{14}\,{\rm M_{\odot}}$ galaxy clusters, and the most massive central galaxies have stellar masses consistent with brightest cluster galaxies (BCGs) progenitors in the TNG300 simulation. The results strongly suggest these structures are forming massive galaxy clusters via baryonic and dark matter accretion.

astro-ph.GA↗