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ABSTRACT A full ring is a form of galaxy morphology that is not associated with a specific stage on the Hubble sequence. Digital sky surveys can collect many millions of galaxy images, and therefore even rare forms of galaxies are expected to be present in relatively large numbers in image data bases created by digital sky surveys. Sloan Digital Sky Survey (SDSS) data release (DR) 14 contains ∼2.6 × 106 objects with spectra identified as galaxies. The method described in this paper applied automatic detection to identify a set of 443 ring galaxy candidates, 104 of them were already included in the Buta + 17 catalogue of ring galaxies in SDSS, but the majority of the galaxies are not included in previous catalogues. Machine analysis cannot yet match the superior pattern recognition abilities of the human brain, and even a small false positive rate makes automatic analysis impractical when scanning through millions of galaxies. Reducing the false positive rate also increases the true negative rate, and therefore the catalogue of ring galaxy candidates is not exhaustive. However, due to its clear advantage in speed, it can provide a large collection of galaxies that can be used for follow-up observations of objects with ring morphology.
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Automatic identification of outliers in Hubble Space Telescope galaxy images
ABSTRACT Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical data bases in the information era, it can be assumed that very many outlier galaxies are included in existing and future astronomical data bases. However, manual search for these objects is impractical due to the required labour, and therefore the ability to detect such objects largely depends on computer algorithms. This paper describes an unsupervised machine learning algorithm for automatic detection of outlier galaxy images, and its application to several Hubble Space Telescope fields. The algorithm does not require training, and therefore is not dependent on the preparation of clean training sets. The application of the algorithm to a large collection of galaxies detected a variety of outlier galaxy images. The algorithm is not perfect in the sense that not all objects detected by the algorithm are indeed considered outliers, but it reduces the data set by two orders of magnitude to allow practical manual identification. The catalogue contains 147 objects that would be very difficult to identify without using automation.
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- Award ID(s):
- 1903823
- PAR ID:
- 10268485
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 501
- Issue:
- 4
- ISSN:
- 0035-8711
- Page Range / eLocation ID:
- 5229 to 5238
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/mnras/staa4036
