46 citations · 92 across the 4 of their papers we have counts for
4 papers
Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features
V. Khramtsov, I. B. Vavilova, D. V. Dobrycheva +5
This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploit…
Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
I. B. Vavilova, V. Khramtsov, D. V. Dobrycheva +3
We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with from the SDSS DR9. We divided i…
New exocomets of Pic
Ya. Pavlenko, I. Kulyk, O. Shubina +3
Aims. The aim of our work is to analyze the light curves of Pic recently observed by TESS in sectors 32, 33, and 34, searching for the signatures of exocomet transits. Methods.…
Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
I. B. Vavilova, D. V. Dobrycheva, M. Yu. Vasylenko +3
Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-N…