Machine learning based data mining for Milky Way filamentary structures reconstruction

TL;DR

This paper introduces FilExSeC, a machine learning tool that enhances the detection and shape reconstruction of filamentary structures in Galactic plane data, improving continuity and detail in filament mapping.

Contribution

The paper presents a novel machine learning approach combining feature extraction and Random Forest classification to refine filament shape reconstruction from Herschel data.

Findings

Method is reliable on simulations and real data

Bridges gaps in filament detection

Potential to refine filament physical parameters

Abstract

We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary "a posteriori" analysis of derived filament physical parameters, the method appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction.