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arXiv 2607.04797astro-ph.GAastro-ph.IM
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Sutra:星际介质中丝状结构识别与表征的集成框架

Sutra : An integrated framework for identification and characterization of filaments in the interstellar medium

Space Applications Centre, ISRO(印度空间研究组织空间应用中心)

Shivam Kumaram, Ushasi Bhowmick, Vipin Kumar, Manish Chauhan, Munn V Shukla, Mehul R Pnadya

AI总结:

研究利用机器学习框架Sutra,通过U-Net架构对柱密度图监督分割,统一丝状结构识别与物理表征,能提取参数并系统研究特性,适用于特定区域和大规模统计分析。

摘要:

对星际介质在远红外和亚毫米波波长的观测揭示了尘埃和气体的复杂丝状结构,其在恒星形成中起关键作用。本文提出Sutra框架,它基于机器学习,将丝状结构识别和束尺度物理表征统一在单个自动化管道中,能进行稳健的丝状结构恢复等。

英文摘要:

Observations of the interstellar medium (ISM) at Far-infrared(FIR) and sub-millimetre (sub-mm) wavelengths reveal a complex filamentary structure of dust and gas, which plays a pivotal role in both low and high mass star formation. Large scale identification and characterization of filaments is crucial to establish a link between the ISM and the star formation. We present Sutra, a machine learning based framework that unifies filament identification and beam-scale physical characterization within a single automated pipeline. The framework employs a U-Net architecture to perform supervised segmentation on column density maps and is trained on five nearby (<500pc) molecular clouds from the Herschel Gould Belt Survey (HGBS), using consensus skeletons constructed from the union of filaments identified by DisPerSE and getsf. Rather than reproducing broad intensity-based masks, Sutra predicts crest-likelihood maps focused on filament spines. Beyond identification, Sutra characterizes the filaments at the beam resolution by extracting radial profiles perpendicular to the crest and deriving local structural parameters. The framework provides a parameter-free, computationally efficient approach for consistent filaments identification and systematic investigation of their local properties and shows stable behaviour across varying background conditions in controlled synthetic tests. We demonstrate its application on selected regions from Aquila, Orion and Polaris molecular clouds, and compare the derived filament characteristics with those obtained using existing algorithms. Sutra robustly recovers filamentary structures consistent with cylindrical profiles, including in relatively low-intensity and low-contrast environments, making it well suited for both region-specific studies and large-scale statistical analyses of early-stage star formation and ISM structure.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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