D
D. Semionov
Publications - 5
Citations - 85
D. Semionov is an academic researcher. The author has contributed to research in topics: Star cluster & Galaxy. The author has an hindex of 3, co-authored 5 publications receiving 85 citations.
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Compact Star Clusters in the M31 Disk
TL;DR: In this paper, a survey of compact star clusters (apparent size 1.5 arcsec (>06 pc) was carried out and the authors derived cluster parameters based on the photometric data and multiband images by employing simple stellar population models.
Journal ArticleDOI
Compact Star Clusters in the M31 Disk
TL;DR: In this paper, a survey of compact star clusters (apparent size 3'') in the southwest part of the M31 galaxy, based on the high-resolution Suprime-Cam images (175 × 285), covering ~15% of the deprojected galaxy disk area.
Journal ArticleDOI
Deriving structural parameters of semi-resolved star clusters - FitClust: a program for crowded fields
D. Narbutis,D. Semionov,R. Stonkutė,P. de Meulenaer,T. Mineikis,A. Bridžius,Vladas Vansevičius +6 more
TL;DR: FitClust as mentioned in this paper is a program that automatically derives the structural parameters of star clusters and estimates errors by accounting for individual stars and variable sky background, and is used to measure clusters of the M31 galaxy in Subaru Suprime-Cam frames.
Posted Content
Radiative transfer problem in dusty galaxies: photometric effects of dusty spiral arms
TL;DR: In this paper, the effects of dusty spiral arms on the photometric properties of disk galaxies using a series of 2D radiative transfer models, approximating the arms with axially symmetrical rings.
Journal ArticleDOI
Deriving structural parameters of semi-resolved star clusters. FitClust: a program for crowded fields
D. Narbutis,D. Semionov,R. Stonkutė,P. de Meulenaer,T. Mineikis,A. Bridžius,Vladas Vansevičius +6 more
TL;DR: FitClust as discussed by the authors is a program that automatically derives the structural parameters of star clusters and estimates errors by accounting for individual stars and variable sky background by fitting the cluster's model within a large radius by using the Levenberg-Marquardt and Markov chain Monte Carlo algorithms.