Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification. (arXiv:2205.04374v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Kwik_R/0/1/0/all/0/1">Robin J. Kwik</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wang_J/0/1/0/all/0/1">Jinfei Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Barmby_P/0/1/0/all/0/1">Pauline Barmby</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Holwerda_B/0/1/0/all/0/1">Benne W. Holwerda</a>
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.
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.
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