Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification

Abstract

Automating classification of galaxy components is important for understanding the formation and evolution of galaxies. Traditionally, only the larger galaxy structures such as the spiral arms, bulge, and disc are classified. Here we use machine learning (ML) pixel-by-pixel classification to automatically classify all galaxy components within digital imagery of massive spiral galaxy UGC 2885. Galaxy components include young stellar population, old stellar population, dust lanes, galaxy center, outer disc, and celestial background. We test three ML models: maximum likelihood classifier (MLC), random forest (RF), and support vector machine (SVM). We use high-resolution Hubble Space Telescope (HST) digital imagery along with textural features derived from HST imagery, band ratios derived from HST imagery, and distance layers. Textural features are typically used in remote sensing studies and are useful for identifying patterns within digital imagery. We run ML classification models with different combinations of HST digital imagery, textural features, band ratios, and distance layers to determine the most useful information for galaxy component classification. Textural features and distance layers are most useful for galaxy component identification, with the SVM and RF models performing the best. The MLC model performs worse overall but has comparable performance to SVM and RF in some circumstances. Overall, the models are best at classifying the most spectrally unique galaxy components including the galaxy center, outer disc, and celestial background. The most confusion occurs between the young stellar population, old stellar population, and dust lanes. We suggest further experimentation with textural features for astronomical research on small-scale galactic structures.

arXiv comment44 pages, 10 figures; Advances in Space Research in press

References in corpus (24)

• The Gaia mission
• Detailed Structural Decomposition of Galaxy Images
• The James Webb Space Telescope
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• Legacy ExtraGalactic UV Survey (LEGUS) with The Hubble Space Telescope. I. Survey Description
• Probabilistic Random Forest: A machine learning algorithm for noisy datasets
• Scientific Discovery with the James Webb Space Telescope
• Machine Learning in Astronomy: a practical overview
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• The PHANGS-HST Survey: Physics at High Angular resolution in Nearby GalaxieS with the Hubble Space Telescope
• The structure of spiral galaxies - II. Near-infrared properties of spiral arms
• Measurement of Galactic Logarithmic Spiral Arm Pitch Angle Using Two-Dimensional Fast Fourier Transform Decomposition
• Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
• Automatic morphological classification of galaxy images
• The spatial distribution of star and cluster formation in M51
• Scalable Automated Detection of Spiral Galaxy Arm Segments
• Search For Star Cluster Age Gradients Across Spiral Arms of Three LEGUS Disk Galaxies
• Rapid sorting of radio galaxy morphology using Haralick features
• Star Formation in Two Luminous Spiral Galaxies
• Quantifying the fine structures of disk galaxies with deep learning:Segmentation of spiral arms in different Hubble types
• Study of star clusters in the M83 galaxy with a convolutional neural network
• Automatic identification of outliers in Hubble Space Telescope galaxy images
• Deconstructing a galaxy: colour distributions of point sources in Messier 83