58 citations · 265 across the 18 of their papers we have counts for
21 papers
Comparing Active Anomaly Detection Frameworks: From Light Curves to Images
Sreevarsha Sreejith, Robert C. Nichol
The increasing volume and complexity of data from modern astronomical surveys are making comprehensive visual inspection increasingly impractical, motivating automated methods for…
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…
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…
Signatures to help interpretability of anomalies
Emmanuel Gangler, Emille E. O. Ishida, Matwey V. Kornilov +8
Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, an…
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,…