Automatic detection of full ring galaxy candidates in SDSS
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 more »
Authors:
Award ID(s):
Publication Date:
NSF-PAR ID:
10167849
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
491
Issue:
3
Page Range or eLocation-ID:
3767 to 3777
ISSN:
0035-8711
2. ABSTRACT We present two catalogues of active galactic nucleus (AGN) candidates selected from the latest data of two all-sky surveys – Data Release 2 of the Gaia mission and the unWISE catalogue of the Wide-field Infrared Survey Explorer (WISE). We train a random forest classifier to predict the probability of each source in the Gaia–unWISE joint sample being an AGN, PRF, based on Gaia astrometric and photometric measurements and unWISE photometry. The two catalogues, which we designate C75 and R85, are constructed by applying different PRF threshold cuts to achieve an overall completeness of 75 per cent (≈90 per cent at GaiaG ≤ 20 mag) and reliability of 85 per cent, respectively. The C75 (R85) catalogue contains 2734 464 (2182 193) AGN candidates across the effective 36 000 deg2 sky, of which ≈0.91 (0.52) million are new discoveries. Photometric redshifts of the AGN candidates are derived by a random forest regressor using Gaia and WISE magnitudes and colours. The estimated overall photometric redshift accuracy is 0.11. Cross-matching the AGN candidates with a sample of known bright cluster galaxies, we identify a high-probability strongly lensed AGN candidate system, SDSS J1326+4806, with a large image separation of 21${^{\prime\prime}_{.}}$06. All the AGN candidates in our catalogues will have ∼5-yr long light curves from Gaiamore »
Low surface brightness (LSB) galaxies are galaxies with central surface brightness fainter than the night sky. Due to the faint nature of LSB galaxies and the comparable sky background, it is difficult to search LSB galaxies automatically and efficiently from large sky survey. In this study, we established the low surface brightness galaxies autodetect (LSBG-AD) model, which is a data-driven model for end-to-end detection of LSB galaxies from Sloan Digital Sky Survey (SDSS) images. Object-detection techniques based on deep learning are applied to the SDSS field images to identify LSB galaxies and estimate their coordinates at the same time. Applying LSBG-AD to 1120 SDSS images, we detected 1197 LSB galaxy candidates, of which 1081 samples are already known and 116 samples are newly found candidates. The B-band central surface brightness of the candidates searched by the model ranges from 22 to 24 mag arcsec−2, quite consistent with the surface brightness distribution of the standard sample. A total of 96.46 per cent of LSB galaxy candidates have an axial ratio (b/a) greater than 0.3, and 92.04 per cent of them have $fracDev\_r$ < 0.4, which is also consistent with the standard sample. The results show that the LSBG-AD model learns the features of LSB galaxiesmore »