Classification of Stellar Age and Galaxy Components within Spiral Galaxies by use of Hubble Space Telescope Imagery and Machine Learning

Galaxies have complex formations of components such as stars, dust, and gas, whose spatial and temporal relationships can help us to better understand the formation and evolution of galaxies, and ultimately the Universe. The main objective of this study is to test how machine learning can be used to classify galaxy components and stellar ages within spiral galaxies based on values of pixels in Hubble Space Telescope imagery, Euclidean distance calculations, textural features, and band ratios. We develop two machine learning models using maximum likelihood, random forest, and support vector machine algorithms. We find the models are successful for classification of galaxy components and stellar age, with Euclidean distance and textural features being the most important parameters. These methods can contribute to the rapid processing of high resolution astronomical imagery of galaxies and other celestial phenomena

Galactic component mapping of galaxy UGC 2885 by machine learning classification

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

Galaxy Mapping by Machine Learning Classification, Lightning Talk (7 min)

My presentation highlights the compatibility of remote sensing and astronomy methods. Here I discuss the classification of galaxy components in UGC 2885, a massive spiral galaxy, by machine learning; in particular, I compare the traditional method of maximum likelihood with the more powerful models random forest and support vector machine

GIS applications in Astronomy, Lightning Talk (7 min)

My presentation will explore similarities between geography and astronomy as well as applications of GIS/remote sensing in the field of astronomy. I will present on a particular method of georeferencing telescope imagery using SAOImageDS9, an astronomy-based software, and open-source QGIS