Identification of Low Surface Brightness Tidal Features in Galaxies Using Convolutional Neural Networks. (arXiv:1811.11616v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Walmsley_M/0/1/0/all/0/1">Mike Walmsley</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ferguson_A/0/1/0/all/0/1">Annette M. N. Ferguson</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mann_R/0/1/0/all/0/1">Robert G. Mann</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lintott_C/0/1/0/all/0/1">Chris J. Lintott</a>
Faint tidal features around galaxies record their merger and interaction
histories over cosmic time. Due to their low surface brightnesses and complex
morphologies, existing automated methods struggle to detect such features and
most work to date has heavily relied on visual inspection. This presents a
major obstacle to quantitative study of tidal debris features in large
statistical samples, and hence the ability to be able to use these features to
advance understanding of the galaxy population as a whole. This paper uses
convolutional neural networks (CNNs) with dropout and augmentation to identify
galaxies in the CFHTLS-Wide Survey that have faint tidal features. Evaluating
the performance of the CNNs against previously-published expert visual
classifications, we find that our method achieves high (76%) completeness and
low (20%) contamination, and also performs considerably better than other
automated methods recently applied in the literature. We argue that CNNs offer
a promising approach to effective automatic identification of low surface
brightness tidal debris features in and around galaxies. When applied to
forthcoming deep wide-field imaging surveys (e.g. LSST, Euclid), CNNs have the
potential to provide a several order-of-magnitude increase in the sample size
of morphologically-perturbed galaxies and thereby facilitate a much-anticipated
revolution in terms of quantitative low surface brightness science.
Faint tidal features around galaxies record their merger and interaction
histories over cosmic time. Due to their low surface brightnesses and complex
morphologies, existing automated methods struggle to detect such features and
most work to date has heavily relied on visual inspection. This presents a
major obstacle to quantitative study of tidal debris features in large
statistical samples, and hence the ability to be able to use these features to
advance understanding of the galaxy population as a whole. This paper uses
convolutional neural networks (CNNs) with dropout and augmentation to identify
galaxies in the CFHTLS-Wide Survey that have faint tidal features. Evaluating
the performance of the CNNs against previously-published expert visual
classifications, we find that our method achieves high (76%) completeness and
low (20%) contamination, and also performs considerably better than other
automated methods recently applied in the literature. We argue that CNNs offer
a promising approach to effective automatic identification of low surface
brightness tidal debris features in and around galaxies. When applied to
forthcoming deep wide-field imaging surveys (e.g. LSST, Euclid), CNNs have the
potential to provide a several order-of-magnitude increase in the sample size
of morphologically-perturbed galaxies and thereby facilitate a much-anticipated
revolution in terms of quantitative low surface brightness science.
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