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

Dana Alina, Adai Shomanov, Sarah Baimukhametova

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)74472-74482
Number of pages11
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Filaments
  • image processing
  • interstellar medium
  • neural networks

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering

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