MaLeFiSenta: Machine Learning for FilamentS Identification and Orientation in the ISM
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Date
2022-07-22
Authors
Alina, Dana
Shomanov, Adai
Baimukhametova, Sarah
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Access
Abstract
Filament identification became a pivotal step in tackling fundamental problems in various
fields of Astronomy. Nevertheless, existing filament identification algorithms are critically user-dependent and require individual parametrization. This study aimed to adapt the neural networks approach to elaborate on 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. We 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...
Description
Keywords
filaments, image processing, interstellar medium, neural networks
Citation
D. Alina, A. Shomanov and S. Baimukhametova, "MaLeFiSenta: Machine Learning for FilamentS Identification and Orientation in the ISM," in IEEE Access, vol. 10, pp. 74472-74482, 2022, doi: 10.1109/ACCESS.2022.3189646.