97 citations · 862 across the 28 of their papers we have counts for
8 papers · 1 filter
Deep Transfer Learning for Classification of Variable Sources
Dae-Won Kim, Doyeob Yeo, Coryn A. L. Bailer-Jones +1
Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe light-curves of billons or more astronomical sources. This presents new challenges for identifying interesting a…
Core-Jet Blending Effects in Active Galactic Nuclei under the Korean VLBI Network View at 43 GHz
Juan-Carlos Algaba, Jeffrey Hodgson, Sin-Cheol Kang +5
A long standing problem in the study of Active Galactic Nuclei (AGNs) is that the observed VLBI core is in fact a blending of the actual AGN core (classically defined by the …
The Power of Simultaneous Multi-frequency Observations for mm-VLBI: Beyond Frequency Phase Transfer
Guang-Yao Zhao, Juan Carlos Algaba, Sang-Sung Lee +15
Atmospheric propagation effects at millimeter wavelengths can significantly alter the phases of radio signals and reduce the coherence time, putting tight constraints on high frequ…
A Package for the Automated Classification of Periodic Variable Stars
Dae-Won Kim, Coryn A. L. Bailer-Jones
We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve…
Interferometric Monitoring of Gamma-ray Bright Active Galactic Nuclei II: Frequency Phase Transfer
Juan-Carlos Algaba, Guang-Yao Zhao, Sang-Sung Lee +11
The Interferometric Monitoring of Gamma-ray Bright Active galactic nuclei (iMOGABA) program provides not only simultaneous multifrequency observations of bright gamma-ray detected…
The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
Dae-Won Kim, Pavlos Protopapas, Coryn A. L. Bailer-Jones +4
The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to detect periodic variable stars in the EROS-2 light curve database. In this paper, we…
An improved quasar detection method in EROS-2 and MACHO LMC datasets
Karim Pichara, Pavlos Protopapas, Dae-Won Kim +2
We present a new classification method for quasar identification in the EROS-2 and MACHO datasets based on a boosted version of Random Forest classifier. We use a set of variabilit…
QSO Selection Algorithm Using Time Variability and Machine Learning: Selection of 1,620 QSO Candidates from MACHO LMC Database
Dae-Won Kim, Pavlos Protopapas, Yong-Ik Byun +3
We present a new QSO selection algorithm using a Support Vector Machine (SVM), a supervised classification method, on a set of extracted times series features including period, amp…