Sutra : An Integrated Framework for Identification and Characterization of Filaments in the Interstellar Medium

Observations of the interstellar medium (ISM) at far-infrared and submillimeter 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 establishing a link between the ISM and 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 (<500 pc) molecular clouds from the Herschel Gould Belt Survey, using consensus skeletons constructed from the combination 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 filament identification and systematic investigation of their local properties and shows stable behavior across varying background conditions in controlled synthetic tests. We demonstrate its application on selected regions from the Aquila, Orion, and Polaris molecular clouds, and compare the derived filament characteristics with those obtained using existing algorithms. Sutr a 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.