14 citations · 40 across the 6 of their papers we have counts for
6 papers
SNAD: enabling discovery in the era of big data
Maria Pruzhinskaya, Emille E. O. Ishida, Konstantin Malanchev +8
In the era of wide-field surveys and big data in astronomy, the SNAD team is exploiting the potential of modern datasets for discovering new, unforeseen, or rare astrophysical obje…
Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker
Miguel Llamas Lanza, Sergey Karpov, Etienne Russeil +5
The detection of tidal disruption events (TDEs) is one of the key science goals of large optical time-domain surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Ve…
Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker
A. Möller, E. E. O. Ishida, J. Peloton +6
Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples t…
Real-bogus scores for active anomaly detection
T. A. Semenikhin, M. V. Kornilov, M. V. Pruzhinskaya +8
In the task of anomaly detection in modern time-domain photometric surveys, the primary goal is to identify astrophysically interesting, rare, and unusual objects among a large vol…
Transient Classifiers for Fink: Benchmarks for LSST
B. M. O. Fraga, C. R. Bom, A. Santos +6
The upcoming Legacy Survey of Space and Time (LSST) is expected to detect a few million transients per night, which will generate a live alert stream during the entire ten years of…
Enabling the discovery of fast transients: A kilonova science module for the Fink broker
B. Biswas, E. E. O. Ishida, J. Peloton +4
We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results…