51 citations · 507 across the 30 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Classification and Clustering Analysis for Variable Stars
R. Pantoja, M. Catelan, K. Pichara +1
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sou…
The VVV Infrared Variability Catalog (VIVA-I)
C. E. Ferreira Lopes, N. J. G. Cross, M. Catelan +17
Thanks to the VISTA Variables in the Via Lactea (VVV) ESO Public Survey it is now possible to explore a large number of objects in those regions. This paper addresses the variabili…
New variable Stars from the Photographic Archive: Semi-automated Discoveries, Attempts of Automatic Classification, and the New Field 104 Her
S. V. Antipin, I. Becker, A. A. Belinski +6
Using 172 plates taken with the 40-cm astrograph of the Sternberg Astronomical Institute (Lomonosov Moscow University) in 1976-1994 and digitized with the resolution of 2400 dpi, w…
Clustering Based Feature Learning on Variable Stars
Cristóbal Mackenzie, Karim Pichara, Pavlos Protopapas
The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical de…
The VVV Templates Project. Towards an Automated Classification of VVV Light-Curves. I. Building a database of stellar variability in the near-infrared
R. Angeloni, R. Contreras Ramos, M. Catelan +50
Context. The Vista Variables in the Vía Láctea (VVV) ESO Public Survey is a variability survey of the Milky Way bulge and an adjacent section of the disk carried out from 2010 on E…
Stellar Variability in the VVV survey
M. Catelan, D. Minniti, P. W. Lucas +34
The Vista Variables in the Vía Láctea (VVV) ESO Public Survey is an ongoing time-series, near-infrared (IR) survey of the Galactic bulge and an adjacent portion of the inner disk,…