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Bin Luo

Publications and source records attributed to Bin Luo.

At least 19 recordsLinked to original sources
arXiv2026

Towards the Generalizability of Leveraging ChatGPT in APR via Self-enhancing: An Empirical Study

Automated Program Repair (APR) increasingly relies on Large Language Models (LLMs). ChatGPT-enhanced APR uses techniques such as self-correction and autonomous agents to improve repair without modifying model parameters. Although these approaches report strong results on Defects4J and SWE-bench, the stability of enhancement gains across benchmarks remains under-explored. We evaluate three ChatGPT-enhanced APR methods on three representative, long-standing benchmarks. With GPT-3.5-Turbo, SRepair achieves a larger absolute gain on HumanEval-Java than on Defects4J, while SRepair and FixAgent without extrinsic information yield negative gains on BugsInPy. With GPT-5.4-mini, the evaluated methods achieve larger absolute gains on Defects4J than on HumanEval-Java, while gains on BugsInPy are non-negative but limited. We investigate benchmark-related factors through code transformations and benchmark-specific fine-tuning. Code transformations reduce enhancement gains on Defects4J, while benchmark-specific fine-tuning increases gains on BugsInPy. Directly supplying GPT-3.5-Turbo with error messages and triggering tests yields more correct repairs than the evaluated ChatGPT-enhanced APR methods on BugsInPy. These findings highlight the need to evaluate generalizability across benchmarks and models using multiple metrics, and suggest that directly providing repair-specific extrinsic information may be more effective than enhancement methods when their gains are limited.

cs.SE↗
arXiv2026

EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning

Audio--visual recordings provide complementary cues for estimating depression severity, but their informativeness varies across time and modalities. Point predictions alone do not express the uncertainty associated with these estimates. We present EviDep, a multimodal evidential regression framework that integrates multi-scale temporal modeling and shared--private representation learning for uncertainty-aware depression estimation. Frequency-aware Feature Extraction decomposes behavioral feature sequences into multiple frequency bands and refines them with scale-specific experts. Disentangled Evidential Learning encourages the disentanglement of cross-modal shared and modality-specific information in the refined features. Multi-branch Evidential Regression maps the resulting shared and private representations to three Normal-Inverse-Gamma (NIG) outputs and uses evidence-weighted aggregation to estimate depression severity and quantify aleatoric and epistemic uncertainty. Experiments on AVEC 2013, AVEC 2014, DAIC-WOZ, and E-DAIC show competitive prediction accuracy, with ablation studies supporting the contributions of frequency-aware refinement and shared--private disentanglement. Further analyses show that estimated epistemic uncertainty helps identify higher-error predictions, while both uncertainty estimates generally increase under controlled feature degradation.

cs.LG↗
arXiv2026

G-ray: Ray-Level Relative Geometric Position Encoding in Multi-View Vision Transformers under Camera Heterogeneity

We study relative position encoding for multi-view vision Transformers under camera heterogeneity, including varying fields of view (FoVs) or projection models. Existing rotary relative position encodings commonly use image-plane positional coordinates, producing projection-dependent relative phases and inconsistent geometric cues for cross-projection attention. We introduce G-ray, a ray-level relative position encoding whose rotary phases are parameterized by camera-local ray angles. The same camera-local ray pair induces the same relative phase across projections, providing projection-invariant positional consistency. G-ray can be used directly or integrated with existing encodings, retaining complementary geometric cues without additional learned parameters. We validate G-ray in three host encodings, RoPE, GTA, and RayRoPE, across 3D reconstruction and novel-view synthesis (NVS). Across three heterogeneous 3D reconstruction benchmarks at 50 views, G-ray leads all six averaged metrics and reduces mean pointmap relative error by 45.8% over MapAnything, with calibration supplied to both. Trained exclusively on homogeneous pinhole images, the 3D reconstruction model handles mixed pinhole and non-pinhole inputs without retraining and remains competitive on homogeneous pinhole 3D reconstruction protocols. For NVS, GTA and RayRoPE improve with G-ray under joint viewpoint and FoV variation. The project's webpage is available at https://g-ray-project.github.io/.

cs.CV↗
arXiv2026

Chandra Lensing-cluster Ultradeep Extragalactic Survey (CLUES) I: A 2 Ms Point-Source Catalog of the Abell 2744 Field

In the first paper of the Chandra Lensing-cluster Ultradeep Extragalactic Survey (CLUES), we present an ultradeep 2 Ms X-ray survey of the Abell 2744 field constructed from 101 archival Chandra ACIS-I observations. With an exposure comparable to the Chandra Deep Fields and further boosted by strong-lensing magnification from the Abell 2744 cluster at $z=0.3$, this field represents the third deepest extragalactic X-ray survey of the sky and also has rich synergy with extensive coverage by the Hubble Space Telescope and James Webb Space Telescope. We present the Chandra data reduction and detect sources in the soft (0.5-2 keV), hard (2-7 keV), and full (0.5-7 keV) bands over a total area of $369~\mathrm{arcmin^2}$. We perform dedicated image fitting with Chandra point spread functions to optimize point-source detections and improve X-ray positions and further screen the detections to address the impact of the central bright, structured intracluster medium. A total of 327 X-ray point sources are detected and cataloged, including their X-ray photometry and basic spectral properties. Detailed simulations are also conducted, based on which the expected 50% flux completeness reaches $6.3\times10^{-16}$, $2.8\times10^{-16}$, and $4.9\times10^{-16}~\mathrm{erg~cm^{-2}~s^{-1}}$ in the full, soft, and hard bands, respectively. The source number density as a function of flux is consistent with those in blank-field surveys within a factor of $\approx2$, with a slight excess above $\approx10^{-14}~\mathrm{erg~cm^{-2}~s^{-1}}$. All X-ray data products are publicly released, including the catalog, X-ray images, exposure maps, background maps, and sensitivity maps.

astro-ph.GA↗
arXiv2026

Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark

RGB-Thermal (RGBT) Video Object Detection (VOD) has gained significant attention because of the limitations of conventional RGB-based VOD methods under challenging conditions, such as low light, heavy fog, and adverse weather, etc. However, spatial misalignment commonly exists between RGBT image pairs. To address this, we propose a Dual-Correlation Hypergraph Network (DCHNet) that captures high-dimensional complementary information by explicitly modeling two types of correlations: temporal correlation across consecutive frames and spatial correlation from cross-modal features. Specifically, we first design a Patch-based Spatial Alignment Module (PSAM) to sequentially align the multimodal features at the local region level. Subsequently, we propose a Dual Hypergraph Fusion Module (DHFM), which constructs temporal and multimodal hypergraphs, respectively, to enhance object characteristic through dual-correlation learning. Furthermore, the field currently lacks a large-scale, scene-diverse benchmark dataset for comprehensive evaluation. Therefore, we construct DVT-VOD1000, a large-scale RGBT VOD dataset containing 1,000 video sequences with 103,464 RGBT image pairs. The dataset covers diverse scenarios, including campuses, parks, traffics, rural areas, night scenes, rainy weather, and snowy weather. Comprehensive experiments on VT-VOD50 and our DVT-VOD1000 demonstrate that DCHNet achieves state-of-the-art detection accuracy. The dataset and source code will be made publicly available on https://github.com/tzz-ahu/ to support academic research.

cs.CV↗
arXiv2026

Systematic Monitoring of Extreme X-ray Variability from Weak-line Quasars

We present the results of a multi-cycle Chandra program to systematically monitor the X-ray variability of 10 weak-line quasars (WLQs) that previously had limited multi-epoch X-ray observations. Three new Chandra 2.8 to 8.2 ks observations were obtained for each WLQ with C$\,$IV rest-frame equivalent widths (REWs) $\lesssim 10$ Å, substantially improving the monitoring data quality of WLQs and our ability to characterize their long-term X-ray variability behavior. We observe recurrent extreme X-ray variability in the historically variable WLQ SDSS J1539+3954, with an X-ray flux rise of a factor of $\gtrsim 6$ between 2023 and 2024 ($\gtrsim 21$ relative to 2013). Another previously X-ray weak WLQ in the sample, SDSS J0825+1155, underwent a significant X-ray flux variation by a factor of $\gtrsim 14$ between 2019 and 2023. We find the fraction of WLQs exhibiting evidence of extreme X-ray variability to be $0.20^{+0.17}_{-0.07}$. In the context of the thick disk and outflow (TDO) model, the substantial fraction of WLQs displaying extreme X-ray variability may suggest that the variability is more likely driven by the intrinsic motion of the TDO wind rather than changes in the height of the TDO disk. We performed a statistical comparison between the distribution of variability amplitudes of WLQs and general radio-quiet quasars. We find that these underlying distributions are statistically different, with WLQs having $\approx 6.8$ times higher odds of exhibiting an extreme X-ray variability event than the general radio-quiet quasar population.

astro-ph.HE↗
arXiv2026

Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise

We study stochastic optimization with heavy-tailed gradient noise. We first propose a novel quantum mean estimator for multivariate heavy-tailed random variables that achieves lower query complexity than optimal classical estimators in the low-dimensional regime. We further develop an unbiased quantum mean estimator by applying a generalized multi-level Monte Carlo technique. We prove quantum lower bounds showing that, when the dimension $d$ of the random vector is small and can be viewed as a constant, our quantum estimators are optimal up to logarithmic factors. We further derive stronger dimension-dependent lower bounds for tail index $p>4/3$, showing that a nontrivial dependence on the dimension is unavoidable in the low-dimensional regime. Based on these estimators, we propose a quantum normalized stochastic gradient descent method ($\texttt{QNSGD}$), which finds an $ε$-stationary point using $\tilde{\mathcal{O}}\big(\sqrt d\,ε^{-\frac{5p-4}{2p-2}}\big)$ queries to the quantum stochastic gradient oracle. For a convex objective function, we propose a quantum projected stochastic gradient descent method ($\texttt{QPSGD}$), which computes a solution with $ε$-optimal solution using $\tilde{\mathcal{O}}\big(\sqrt d\,ε^{-\frac{3p-2}{2p-2}}+ε^{-2}\big)$ queries in expectation. These sharper bounds improve upon the classical lower bounds $Ω\big(ε^{-\frac{3p-2}{p-1}}\big)$ for nonconvex problems and $Ω\big(ε^{-\frac{p}{p-1}}\big)$ for convex problems in the low-dimensional regimes $d\lesssimε^{-\frac{p}{p-1}}$ and $d\lesssimε^{-\frac{2-p}{p-1}}$, respectively.

cs.LG↗
arXiv2026

Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking

Missing modalities in RGBT tracking often lead to incomplete and unstable multimodal feature representations that greatly degrade the performance. Existing methods typically attempt to recover missing modalities from available ones, but the quality of data generated in challenging scenarios might be unsatisfactory. In addition, current approaches exhibit limited flexibility in processing both missing and complete data. To overcome these limitations, we propose a Spatio-temporal Conditional Denoising Transformer (SCDT), which integrates the spatial cues and the temporal context to adaptively perform information reconstruction of missing modalities and feature enhancement of weak modalities in a unified framework, for robust modality-missing RGBT tracking. In particular, SCDT leverages the short-term temporal cues from recent historical frames to capture the fine-grained temporal correlations and the long-term temporal cues encoding modality evolution to capture the global context. By jointly exploiting long short-term temporal contexts as the conditions, SCDT progressively guides noisy features of available modalities to learn reliable and temporally consistent multimodal representations. Furthermore, SCDT introduces a noisemodulated adaptation mechanism that dynamically adjusts its behavior according to the modal availability, enabling a single framework to unify feature learning under both modality-missing and complete scenarios without changing the architecture or parameters. Extensive experiments on three public benchmark datasets demonstrate that our method consistently outperforms state-of-the-art methods. The code is available here.

cs.CV↗
arXiv2026

ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection

The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy. To address this, we propose a novel, end-to-end adversarial spatio-frequency refinement network (ASFR-Net). Initially, a modality-invariant representation learner (MIR-Learner) guides the backbone to extract modality-invariant features, effectively bridging the primary domain gap. Subsequently, to address persistent residual modal differences, we design an innovative spatio-frequency synergistic enhancement module (SFEM), which identifies and suppresses sensor-specific noise and artifacts that are difficult to discern in the spatial domain by leveraging frequency-domain processing. Multi-level difference features are then computed from these refined representations and fed into a decoder equipped with cascaded hierarchical guided fusion module (HGFM) blocks to generate precise change maps. To alleviate the data scarcity in heterogeneous tasks, we construct and release a new high-resolution benchmark specifically focused on building changes: the visible-near-infrared heterogeneous change detection (VisNIR-HCD) dataset. It presents unique scientific challenges arising from deceptive visual similarity and non-linear spectral inversions, providing a robust platform for evaluating model generalization. Extensive experiments on VisNIR-HCD and public datasets demonstrate that ASFR-Net achieves state-of-the-art (SOTA) performance, significantly outperforming existing methods. The source code and the VisNIR-HCD dataset are publicly available at https://github.com/LuoYang2024/ASFR-Net.

cs.CV↗
arXiv2026

Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference

Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation. Existing acceleration methods usually remove visual tokens or skip visual-token updates in entire layers, but these coarse strategies may discard fine-grained evidence or suppress useful operators together with redundant ones. In this paper, we study visual-token computation from an answer-observable perspective and find that late visual-token updates can remain large while having little effect on answer-token representations. Motivated by this answer-silent redundancy, we decompose each Transformer layer into attention and FFN operators and show that useful visual computation is often operator-dominant and layer-dependent. We propose an operator-level visual-token skipping framework that preserves the full visual-token sequence while selectively bypassing redundant attention, FFN, or both. Experiments across three MLLM architectures and 10 VQA benchmarks show that our method achieves strong efficiency-accuracy trade-offs, reducing \textbf{33.7\%} TFLOPs on Qwen3-VL while retaining \textbf{99.5\%} of the vanilla model performance.

cs.CV↗
arXiv2026

Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution

Diffusion-based methods demonstrate significant potential for remote sensing image super-resolution at large scaling factors, particularly in reference-based super-resolution (RefSR), where high-resolution reference images provide critical fine-grained texture priors. However, existing methods often suffer from a trade-off between over-reliance on reference information, which leads to texture artifacts, and under-utilization of such information, which results in insufficient detail recovery. To address these issues, we propose DS-DiT, a Decoupled Siamese Diffusion Transformer that decouples the interaction between low-resolution (LR) and reference (Ref) conditions within the attention mechanism. By allowing LR structural priors and Ref texture information to independently interact with the noisy latent, the framework effectively mitigates competition between the two conditional sources. To further compensate for the limited local modeling ability of global attention, we introduce a Patch-Level Weighting (PLW) module that adaptively modulates the fusion of conditional sources. In addition, the siamese architecture enables an inference-time autoguidance strategy that exploits the prediction discrepancy between strong and weak Ref conditions to improve generation quality without additional training. Experimental results across multiple datasets and scaling factors show that DS-DiT outperforms existing methods in both quantitative metrics and visual fidelity.

cs.CV↗
arXiv2026

UNICS: Multilingual Code Search via Unified Pseudocode and Contrastive Transfer Learning

While pre-trained models have achieved remarkable success in code search, their multilingual capabilities remain a major hurdle, plagued by data imbalance, cross-lingual semantic interference, and the loss of critical information from existing unified representations like Abstract Syntax Trees (ASTs) or Intermediate Representations (IRs). Furthermore, conventional contrastive learning strategies often rely on simplistic hard negative sampling while overlooking the potential of mining hard positives to learn code's intrinsic semantic invariance. To address these challenges, we introduce UNICS, a framework for multilingual code search built on a two-stage training strategy. In the first stage, UNICS is pre-trained on a novel dataset we constructed, which uses pseudo-code as a unified representation to learn a cross-lingual, algorithm-level logic that preserves full semantic fidelity. The second stage employs a multi-task transfer learning strategy that adapts this general knowledge to specific languages by decomposing code into semantic slices (e.g., API calls, function bodies) and incorporating tasks for hard positive mining and cross-lingual dynamic hard negative sampling. Experimental results demonstrate that UNICS achieves state-of-the-art performance across multiple multilingual and cross-lingual benchmarks, showcasing superior generalization and performance balance, especially in zero-shot transfer tasks to low-resource languages.

cs.SE↗
arXiv2026

B Meson Semi-Invisible Decays via Perturbative QCD

This paper focuses on the dark sector decay processes of $B$ mesons ($B\to \mathcal{B}_8\ +$ invisible). Using the perturbative QCD (pQCD) approach combined with flavor symmetry analysis, we calculate the branching ratios for decays from $B$ mesons into light baryons and dark baryons within two distinct $B$-Mesogenesis scenarios. A detailed discussions of the form factor $B\to \mathcal{B}_8$ are presented. Based on the derived form factors and effective couplings, we then reach the final numerical analysis. The results show that the branching ratios are sizable, especially for $B^0\to Λψ$ and $B_s^0\toΞ^0ψ$ in Type-I model, with values on the order of $\mathcal{O}(10^{-5})$ . Such processes are expected to facilitate the search for dark matter at hadron colliders and B factories.

hep-ph↗
arXiv2026

Strong X-ray Variability of I Zwicky 1: Obscuration from Clumpy Accretion-Disk Winds

Obscuration from clumpy accretion-disk winds has been invoked to explain the extreme X-ray weakness and X-ray variability observed in a substantial fraction of super-Eddington accreting quasars. We present a comprehensive study of the strong X-ray variability of the super-Eddington accreting active galactic nucleus (AGN) I Zwicky 1 (I Zw 1), a prototypical narrow-line} Seyfert 1 galaxy (NLS1), to test the disk-wind obscuration scenario as the underlying mechanism and characterizing the disk-wind absorber properties. We focus on spectral and temporal analyses of simultaneous XMM-Newton and NuSTAR observations in 2020, and a 100-day NICER monitoring campaign in 2022. Despite strong X-ray variability by factors of $\approx3$ and $\approx6$ on short-term and long-term timescales, respectively, the XMM-Newton Optical Monitor observations do not show contemporaneous significant UV variability, and archival data reveal only mild long-term optical/infrared variability ($\approx30\%$), indicating a stable accretion process in I Zw 1. The strong X-ray variability thus likely arises from variable absorption of relatively stable coronal emission. We perform time-resolved X-ray spectroscopy utilizing a partial-covering absorption model with a stable corona and varying ionized absorbers. We identify three distinct absorbers whose variations in the column density and covering factor successfully explain the observed X-ray ``flares'' in 2020 and the longer-term spectral evolution in 2022. Our results support a unified scenario in which obscuration from clumpy disk winds produces the strong X-ray variability observed in super-Eddington accreting AGNs. This scenario may be applicable to other NLS1s exhibiting strong X-ray variability to better characterize the disk winds driven by super-Eddington accretion.

astro-ph.HE↗
arXiv2026

SARU: A Shadow-Aware and Removal Unified Framework for Remote Sensing Images with New Benchmarks

Shadows are a prevalent problem in remote sensing imagery (RSI), degrading visual quality and severely limiting the performance of downstream tasks like object detection and semantic segmentation. Most prior works treat shadow detection and removal as separate, cascaded tasks, which can lead to cumbersome process and error accumulation. Furthermore, many deep learning methods rely on paired shadow and non-shadow images for training, which are often unavailable in practice. To address these challenges, we propose Shadow-Aware and Removal Unified (SARU) Framework , a cohesive two-stage framework. First, its dual-branch detection module (DBCSF-Net) fuses multi-color space and semantic features to generate high-fidelity shadow masks, effectively distinguishing shadows from dark objects. Then, leveraging these masks, a novel, training-free physical algorithm (N$^2$SGSR) restores illumination by transferring properties from adjacent non-shadow regions within the single input image. To facilitate rigorous evaluation and foster future work, we also introduce two new benchmark datasets: the RSI Shadow Detection (RSISD) dataset and the Single-image Shadow Removal Benchmark (SiSRB). Extensive experiments on the AISD and RSISD datasets demonstrate that SARU achieves SOTA shadow detection performance. For shadow removal, our training-free N$^2$SGSR algorithm attains an average processing speed of approximately $1.3$s, which is over $10$ times faster than the SOTA MAOSD while maintains an SRI value close to 0.9 on both the AISD and SiSRB datasets, a level comparable to the advanced RS-GSSR method. By holistically integrating shadow detection and removal to mitigate error propagation and eliminating the dependency on paired training data, SARU establishes a robust, practical framework for real-world RSI analysis. The code and datasets are publicly available at: https://github.com/AeroVILab-AHU/SARU

cs.CV↗
arXiv2026

An Extensive Replication Study of the ABLoTS Approach for Bug Localization

Bug localization is the task of recommending source code locations (typically files) that contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components (e.g., similar reports, version history, and code structure) to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports (i.e., feature requests and bug reports) to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, we conducted a replication study of this approach with the original dataset and also on two extended datasets (i.e., additional Java dataset and Python dataset). The original dataset consists of 11 open source Java projects with 8,494 bug reports. The extended Java dataset includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. The extended Python dataset consists of 12 projects with 1,289 bug reports. While we find that the TraceScore component, which is the core of ABLoTS, produces comparable or even better results with the extended datasets, we also find that we cannot reproduce the ABLoTS results, as reported in its original paper, due to an overlooked side effect of incorrectly choosing a cut-off date that led to test data leaking into training data with significant effects on performance.

cs.SE↗
arXiv2026

Dynamic Pondering Sparsity-aware Mixture-of-Experts Transformer for Event Stream based Visual Object Tracking

Despite significant progress, RGB-based trackers remain vulnerable to challenging imaging conditions, such as low illumination and fast motion. Event cameras offer a promising alternative by asynchronously capturing pixel-wise brightness changes, providing high dynamic range and high temporal resolution. However, existing event-based trackers often neglect the intrinsic spatial sparsity and temporal density of event data, while relying on a single fixed temporal-window sampling strategy that is suboptimal under varying motion dynamics. In this paper, we propose an event sparsity-aware tracking framework that explicitly models event-density variations across multiple temporal scales. Specifically, the proposed framework progressively injects sparse, medium-density, and dense event search regions into a three-stage Vision Transformer backbone, enabling hierarchical multi-density feature learning. Furthermore, we introduce a sparsity-aware Mixture-of-Experts module to encourage expert specialization under different sparsity patterns, and design a dynamic pondering strategy to adaptively adjust the inference depth according to tracking difficulty. Extensive experiments on FE240hz, COESOT, and EventVOT demonstrate that the proposed approach achieves a favorable trade-off between tracking accuracy and computational efficiency. The source code will be released on https://github.com/Event-AHU/OpenEvTracking.

cs.CV↗
arXiv2026

A Rare Eddington-Limited, Heavily Obscured Low-Mass Active Galactic Nucleus Likely Triggered by a Galaxy Merger

We report a detailed analysis of GAMA 376183, a powerful, heavily obscured active galactic nucleus (AGN) hosted by a low-mass galaxy ($M_\star \approx 10^{10}~M_{\odot}$) likely experiencing a galaxy merger. The source was initially identified due to its remarkably strong [Ne v] $\lambda3426$ emission, exhibiting a rest-frame equivalent width (EW) of $\approx 48$ A. We present $\sim100$ ks Nuclear Spectroscopic Telescope Array follow-up observations, confirming its heavily obscured nature with a column density (in $\mathrm{cm^{-2}}$) of $\log N_\mathrm{H} = 23.3^{+0.4}_{-1.2}$ and an intrinsic $2$--$10$ keV luminosity (in $\mathrm{erg~s^{-1}}$) of $\log L_\mathrm{X,int} = 42.92^{+0.24}_{-0.20}$. GAMA 376183 thus represents one of the few known heavily obscured AGNs in low-mass galaxies. Its estimated Eddington ratio is $λ_\mathrm{Edd}\approx0.8$, indicative of rapid black-hole growth. High-resolution optical images reveal a disturbed, likely merging morphology, while its multiwavelength spectral energy distribution indicates a recent starburst in its host galaxy. These pieces of evidence suggest that the ongoing merger has triggered both the heavily obscured, Eddington-limited accretion and the starburst, making GAMA 376183 a rare observed case in low-mass galaxies. Overall, this unique source demonstrates that (i) [Ne v] can help identify heavily obscured low-mass AGNs, and (ii) the merger-driven coevolution framework established for massive galaxies may also extend to low-mass galaxies.

astro-ph.GA↗