VizieR Online Data Catalog: Galaxies at 0.02<z<0.1 morphological catalog (Vavilova+, 2022)
Abstract
The morphological catalog of 315 776 galaxies at 0.02<z<0.1 with the absolute stellar magnitudes in the range of -24...-13 at z<0.1 from the SDSS DR9 is obtained by human labeling, multi-photometry, supervised machine learning methods, and CNN classifier. For the photometric binary morphological classification, we used absolute magnitudes Mu, Mg, Mr, Mi, Mz; color indices Mu-Mr, Mg-Mi, Mu-Mg, Mr-Mz; and the inverse concentration index to the center R50/R90. The supervised methods provide the accuracy of 96.4% for Support Vector Machine (96.1% early and 96.9% late types) and 95.5% for Random Forest (96.7% early and 92.8% late types). To obtain the CNN image-based classification of morphological classes and features, we divided 315782 galaxies into two subsamples, SDSS DR9 galaxy dataset and Galaxy Zoo 2 (GZ2) dataset, considering them as the inference and training datasets, respectively. When training the CNN classifier for a more accurate result, we took into consideration only those galaxies for which GZ2's volunteers gave the most votes. The criteria for each image of the galaxy are defined in the GZ2 project, their description is available through web-site https://data.galaxyzoo.org/.
The accuracy of CNN-classifier on the morphological classes is as follows: cigar-shaped (75%), completely round (83%), round in-between (93%), edge-on (93%), spiral (96%). As for the classification of galaxies by their detailed 32 structural morphological features, our CNN model gives the accuracy in the range of 83.3-99.4% depending on features (bar, rings, number of spiral arms, mergers, dust lane, edge-on, etc.), a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. As a result, for the first time, we assigned the detailed morphological classification for ~140 000 low-redshift galaxies, especially at the fainter end mr <17.7. Galaxies at 0.02<z<0.1 morphological catalog v.2 (Vavilova et al., 2022KNIT...28....3V) is available in CSV format at ftp://ftp.mao.kiev.ua/pub/astro/cats/galaxies/ galSDSSDR9zto0.1morph_classification.csv. (1 data file).- Publication:
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VizieR Online Data Catalog (other)
- Pub Date:
- February 2023
- Bibcode:
- 2023yCatp071002801V
- Keywords:
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- Galaxy catalogs;
- Morphology;
- Photometry: SDSS