MaLeFiSenta: Machine Learning for FilamentS Identification and orientation in the ISM

Abstract

Filament identification became a key step to tackling fundamental problems in various fields of Astronomy. Nevertheless, existing filament identification algorithms are critically user-dependent and require individual parametrization. In this study, we aimed at adapting the neural networks approach to elaborate the best model for filament identification that would not require fine-tuning for a given astronomical map. First, we created training samples based on the most commonly used maps of the interstellar medium obtained by Planck and Herschel space telescopes and the atomic hydrogen all-sky survey HI4PI. We used the Rolling Hough Transform, a widely used algorithm for filament identification, to produce training outputs. In the next step, we trained different neural network models and discovered that a combination of the Mask R-CNN and U-Net architecture is most appropriate for filament identification and determination of their orientation angles. We showed that neural network training might be performed efficiently on a relatively small training sample of only around 100 maps. Our approach eliminates the parametrization bias and facilitates filament identification and angle determination on large data sets.

11 pages, 7 figures, 3 tables. Published in IEEE Access

References in corpus (7)

• HI4PI: A full-sky HI survey based on EBHIS and GASS
• FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation
• The identification of filaments on far infrared and submillimiter images. Morphology, physical conditions and relation with star formation of filamentary structure
• The $AKARI$ Far-Infrared All-Sky Survey Maps
• Learning to Denoise Astronomical Images with U-nets
• Measuring solar active region inflows with local correlation tracking of granulation
• Template matching method for the analysis of interstellar cloud structure

Cited by in corpus (4)

• Nature of Striation in 21 cm Channel Maps: Velocity Caustics
• Statistical properties of filaments in the cosmic web
• GrAviPaSt's Lens to the Past: Unveiling the Evolution of Filamentary Structures
• Interstellar filament detection and characterization: methods and implications for studies of magnetized interstellar medium