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Zheng Ma

Zheng Ma contributes to research discovery and scholarly infrastructure.

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Published work

4 published item(s)

preprint2026arXiv

Abundant Population of Broad H$α$ Emitters in the GOODS-N Field Revealed by CONGRESS, FRESCO, and JADES

We present a spectroscopic search for broad H$α$ emitters at z$\approx$3.7-6.5 in the GOODS-N field, utilizing JWST/NIRCam slitless spectroscopy from FRESCO and CONGRESS, complemented by JADES imaging. We identify 19 broad H$α$ emitters with FWHM$>$1000 km/s at z$\approx$4-5.5, including 9 new sources. The broad H$α$ luminosity function (LF) derived from our sample is consistent with those of other JWST-selected broad-line AGN reported in the literature. The black hole masses and AGN bolometric luminosities, inferred from the broad H$α$ components, indicate that most sources are accreting at ~10% of the Eddington limit. We derive their host stellar masses via SED fitting and find higher $M_{BH}/M_{*}$ ratios relative to the local $M_{BH}-M_{*}$ relations, consistent with previous studies. We find that 42% of the sample do not satisfy the widely-used color selection criteria for Little Red Dots (LRDs), with the majority of these sources lacking the characteristic steep rest-optical red slope, indicating that the LRD selection is highly incomplete when selecting AGN galaxies. A comparison of the average SEDs between our sample and LRDs selected in the same field reveals that the steep red slopes observed in some LRDs are likely due to line-boosting effects as previously suggested. Furthermore, we find that 68% of color-selected LRDs with H$α$ detections in the NIRCam/Grism spectra do not exhibit broad-line features. While the limited sensitivity of the grism spectra may hinder the detection of broad-line components in faint sources, our findings still highlight the enigmatic nature of the LRD population.

preprint2026arXiv

Asymptotic-Preserving Neural Networks based on Even-odd Decomposition for Multiscale Gray Radiative Transfer Equations

We present a novel Asymptotic-Preserving Neural Network (APNN) approach utilizing even-odd decomposition to tackle the nonlinear gray radiative transfer equations (GRTEs). Our AP loss demonstrates consistent stability concerning the small Knudsen number, ensuring the neural network solution uniformly converges to the diffusion limit solution. This APNN method alleviates the rigorous conservation requirements while simultaneously incorporating an auxiliary deep neural network, distinguishing it from the APNN method based on micro-macro decomposition for GRTE. Several numerical problems are examined to demonstrate the effectiveness of our proposed APNN technique.

preprint2026arXiv

Empowering Small Language Models with Factual Hallucination-Aware Reasoning for Financial Classification

Small language models (SLMs) are increasingly used for financial classification due to their fast inference and local deployability. However, compared with large language models, SLMs are more prone to factual hallucinations in reasoning and exhibit weaker classification performance. This raises a natural question: Can mitigating factual hallucinations improve SLMs' financial classification? To address this, we propose a three-step pipeline named AAAI (Association Identification, Automated Detection, and Adaptive Inference). Experiments on three representative SLMs reveal that: (1) factual hallucinations are positively correlated with misclassifications; (2) encoder-based verifiers effectively detect factual hallucinations; and (3) incorporating feedback on factual errors enables SLMs' adaptive inference that enhances classification performance. We hope this pipeline contributes to trustworthy and effective applications of SLMs in finance.

preprint2026arXiv

GeoSym127K: Scalable Symbolically-verifiable Synthesis for Multimodal Geometric Reasoning

Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain-of-Thought (CoT) data. To address this, we propose the GeoSym Engine, an automated and scalable neuro-symbolic framework. By leveraging a type-conditional grammar and an analytic SymGT Solver, it derives exact symbolic ground truths and seamlessly integrates with a robust rendering pipeline to produce high-precision geometric diagrams. Using this engine, we construct GeoSym127K, a difficulty-stratified dataset featuring 51K high-resolution images, 127K questions with symbolic ground truths, and 55K answer-verified CoT QA pairs. We also introduce GeoSym-Bench, an expert-curated suite of 511 complex samples for rigorous evaluation. Through extensive supervised fine-tuning (SFT), we demonstrate that GeoSym drives concentrated improvements specifically on diagram-dependent and multi-step geometry tasks. Our Qwen3-VL-8B model gains an absolute +22.21% on the MathVerse Vision-Only subset and reaches 61.52% (+6.19% improvement) on WeMath, mitigating long-horizon logic fragmentation and outperforming advanced closed-source models like Doubao-1.8. Furthermore, applying Reinforcement Learning with Verifiable Rewards (RLVR) via GRPO reveals that initializing from structural SFT checkpoints substantially elevates the performance ceiling over zero-shot RL. Driven by deterministic exact-match signals, this showcases the robust scaling potential of our verifiable reasoning synthesis. Datasets and code are available at https://huggingface.co/datasets/Tomie0506/GeoSym127K and https://github.com/Tomie56/GeoSym127K.