47 citations · 110 across the 6 of their papers we have counts for
7 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…
KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
Vladislav Khramtsov, Alexey Sergeyev, Chiara Spiniello +9
The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at finding as many previously undiscovered gravitational lensed quasars as possible in the Kilo Degree Survey. T…
KiDS0239-3211: A new gravitational quadruple lens candidate
A. Sergeyev, C. Spiniello, V. Khramtsov +5
We report the discovery of a candidate to quadrupole gravitationally lensed system KiDS0239-3211 based on the public data release 3 of the KiDS survey and machine learning techniqu…
Machine-learning identification of extragalactic objects in the optical-infrared all-sky surveys
Vladislav Khramtsov, Volodymyr Akhmetov
We present new fully-automatic classification model to select extragalactic objects within astronomy photometric catalogs. Construction of the our classification model is based on…
New algorithm for astrometric reduction of the wide-field images
Volodymyr Akhmetov, Sergii Khlamov, Vladislav Khramtsov +1
In this paper we presented the modified algorithm for astrometric reduction of the wide-field images. This algorithm is based on the iterative using of the method of ordinary least…
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…