BRAHMa：基于机器学习的棒状结构识别与勾画——面向星系棒定向包围盒检测的框架
BRAHMa: Bar Recognition And Hatching using Machine learning -- a framework for oriented bounding box detection of galactic bars
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- Maulana Azad National Institute of Technology (MANIT)（毛拉纳·阿扎德国家理工学院（MANIT））
- Indian Institute of Technology (IIT) Indore（印度理工学院印多尔分校（IIT Indore））
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
提出BRAHMa框架，基于YOLO11x定向包围盒模型，利用合成数据与GZ-3D真实数据训练，实现星系棒快速客观检测，在3,150个星系上达到1.1 kpc的L1误差，接近目录间差异极限，支持大规模形态学研究。
中文摘要 AI 辅助
星系棒是暗物质、星系演化和长期动力学研究中的基本组成部分。然而，当前测量星系棒属性的方法存在显著局限性。人工和众包编目无法扩展到下一代巡天，而近期基于像素级分割的机器学习（ML）方法计算强度大，且需要复杂的后处理来提取物理参数。在本文中，我们介绍了BRAHMa（基于机器学习的棒状结构识别与勾画），一个基于定向包围盒YOLO11x模型的公开可用工具，旨在快速、客观地检测星系棒。我们的模型首先在合成数据（Shrivastava 2025）上训练，验证其有效性后再使用真实数据进行训练。BRAHMa在来自DESI Legacy Survey的图像上，利用Galaxy Zoo 3D（GZ-3D）公民科学项目的棒状掩模进行训练。我们通过将GZ-3D标签与Hoyle数据集中的586个共同星系进行比较，解决了天文数据中固有的“地面真值”挑战。这一比较得出了GZ3D衍生测量与Hoyle测量之间0.99 kpc的基线平均绝对误差，为目录间差异提供了经验参考。我们的BRAHMa工具在Hoyle数据集的3,150个星系的大样本上实现了1.1 kpc的L1误差，展示了接近这一基本极限的性能。我们进一步通过将模型应用于不同数据集来验证其鲁棒性，证明了其泛化能力。BRAHMa为星系棒提取提供了一种可扩展且可靠的方法，支持大规模形态学研究。该工具可通过BRAHMa网页界面公开获取。（此https URL）
英文摘要
Galactic bars are fundamental components in studies of dark matter, galaxy evolution, and secular dynamics. However, current methods for measuring bar properties suffer from significant limitations. Manual and crowdsourced catalogs are not scalable to next-generation surveys, while recent machine learning (ML) approaches based on pixel-wise segmentation are computationally intensive and require complex post-processing to extract physical parameters. In this paper, we introduce BRAHMa (Bar Recognition And Hatching using Machine Learning), a publicly available tool based on an oriented-bounding-box YOLO11x model, designed for the rapid and objective detection of galactic bars. Our model was initially trained on synthetic data (Shrivastava 2025), validating its efficacy before training with real data. BRAHMa was trained on bar masks from the Galaxy Zoo 3D (GZ-3D) citizen science project, utilizing images from the DESI Legacy Survey. We address the inherent challenge of 'ground truth' in astronomical data by comparing GZ-3D labels with the Hoyle dataset for 586 common galaxies. This comparison yields a baseline mean absolute error of 0.99 kpc between the GZ3D-derived and Hoyle measurements for 586 common galaxies, providing an empirical reference for catalogue-to-catalogue disagreement. Our BRAHMa tool achieves an L1 error of 1.1 kpc on a large set of 3,150 galaxies from the Hoyle dataset, demonstrating performance that approaches this fundamental limit. We further validate the model's robustness by applying it to distinct datasets, proving its generalizability. BRAHMa provides a scalable and reliable method for bar extraction, enabling large-scale morphological studies. The tool is publicly available through the BRAHMa web interface. (https://brahma-bar-detector.netlify.app/)
