10 citations · 14 across the 9 of their papers we have counts for
8 papers · 1 filter
Supernova scores for active anomaly detection
Semenikhin T. A., Kornilov M. V., Pruzhinskaya M. V. +3
Large time-domain sky surveys generate extensive multi-year catalogs of light curves in which scientifically valuable transients, such as supernovae (SNe), are vastly outnumbered b…
What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23
Maria V. Pruzhinskaya, Anastasia D. Lavrukhina, Timofey A. Semenikhi +7
We present results from the SNAD VIII Workshop, during which we conducted the first systematic anomaly search in the ZTF fields also observed by LSSTComCam during Rubin Scientific…
Dataset of artefacts for machine learning applications in astronomy
Sreevarsha Sreejith, Maria V. Pruzhinskaya, Alina A. Volnova +8
Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this ta…
Coniferest: a complete active anomaly detection framework
M. V. Kornilov, V. S. Korolev, K. L. Malanchev +8
We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently,…
Superluminous supernova search with PineForest
T. Majumder, M. V. Pruzhinskaya, E. E. O. Ishida +2
The advent of large astronomical surveys has made available large and complex data sets. However, the process of discovery and interpretation of each potentially new astronomical s…
Exploring the Universe with SNAD: Anomaly Detection in Astronomy
Alina A. Volnova, Patrick D. Aleo, Anastasia Lavrukhina +9
SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. T…