Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Narbutis D."'
Publikováno v:
Open Astronomy, Vol 24, Iss 3, Pp 305-313 (2015)
We study the impact of photometric signal to noise on the accuracy of derived structural parameters of unresolved star clusters using MCMC model fitting techniques. Star cluster images were simulated as a smooth surface brightness distribution follow
Externí odkaz:
https://doaj.org/article/029eba2d5f1c4978a35eb903a067b2ac
Publikováno v:
Open Astronomy, Vol 24, Iss 3, Pp 293-297 (2015)
We report a serendipitous discovery of a star cluster in the dwarf irregular galaxy Leo A. Young age (~28 Myr) and low mass (~510 M⊙) estimates are based on the isochrone fit assuming a metallicity derived for HII regions (Z = 0.0007). The color-ma
Externí odkaz:
https://doaj.org/article/709dda6307104d399a752994fa00755a
Publikováno v:
Open Astronomy, Vol 23, Iss 3-4, Pp 199-207 (2014)
This paper aims to contribute to the debate taking place nowadays on the two extreme schemes of sampling the stellar masses within star clusters, known as Optimal Sampling and Random Sampling. We propose a new method for sampling of stellar masses in
Externí odkaz:
https://doaj.org/article/29eeb009e27344a7b579ac4f22c14e34
Publikováno v:
Open Astronomy, Vol 23, Iss 2, Pp 103-109 (2014)
Stochasticity of bright stars introduces uncertainty and bias into derived structural parameters of star clusters. We have simulated a grid of cluster V-band images, observed with the Subaru Suprime-Cam, with age, mass and size representing a cluster
Externí odkaz:
https://doaj.org/article/0f0bf7df3b334435a280497603c03e07
Autor:
Bialopetravičius, J., Narbutis, D.
We present a study of evolutionary and structural parameters of star cluster candidates in the spiral galaxy M83. For this we use a convolutional neural network trained on mock clusters and capable of fast identification and localization of star clus
Externí odkaz:
http://arxiv.org/abs/2010.11126
Autor:
Bialopetravičius, J., Narbutis, D.
Publikováno v:
A&A 633, A148 (2020)
Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification on natural images. This opens the door for astrophysical parameter inference on the exponentially increasing amount o
Externí odkaz:
http://arxiv.org/abs/1911.10059
Publikováno v:
A&A 621, A103 (2019)
Context. Convolutional neural networks (CNNs) have been proven to perform fast classification and detection on natural images and have potential to infer astrophysical parameters on the exponentially increasing amount of sky survey imaging data. The
Externí odkaz:
http://arxiv.org/abs/1807.07658
Publikováno v:
A&A 581, A111 (2015)
Context. When trying to derive the star cluster physical parameters of the M33 galaxy using broad-band unresolved ground-based photometry, previous studies mainly made use of simple stellar population models, shown in the recent years to be oversimpl
Externí odkaz:
http://arxiv.org/abs/1507.08494
Publikováno v:
ApJS 214, 19 (2014)
We have surveyed a complete extent of Leo A - an apparently isolated gas-rich low-mass dwarf irregular galaxy in the Local Group. The $B$, $V$, and $I$ passband CCD images (typical seeing $\sim$0.8") were obtained with Subaru Telescope equipped with
Externí odkaz:
http://arxiv.org/abs/1410.2542
Autor:
Narbutis, D., Semionov, D., Stonkutė, R., de Meulenaer, P., Mineikis, T., Bridžius, A., Vansevičius, V.
Publikováno v:
A&A 569, A30 (2014)
Context. An automatic tool to derive structural parameters of semi-resolved star clusters located in crowded stellar fields in nearby galaxies is needed for homogeneous processing of archival frames. Aims. We have developed a program that automatical
Externí odkaz:
http://arxiv.org/abs/1410.2514