SNAD: enabling discovery in the era of big data
Abstract
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 objects and phenomena with machine learning (ML). The SNAD pipeline was built under the hypothesis that, although automatic ML algorithms have a crucial role to play in this task, the scientific discovery is only completely realized when such systems are designed to boost the impact of domain knowledge experts. Our key contributions include the development of the Coniferest Python library, which offers implementations of two active learning algorithms with an ``expert in loop'', and the creation of the SNAD Transient Miner, facilitating the search for specific types of transients. We have also developed the SNAD Viewer, a web portal that provides a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, making the analysis of potential anomalies more efficient. Finally, when applied to ZTF data, our approach has resulted in more than a hundred new supernova (SN) candidates, along with a few other non-catalogued objects, such as red dwarf flares, superluminous SNe, RS CVn type variables, and young stellar objects.
Versions
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BibTeX
@misc{pruzhinskaya2026snad,
title = {{SNAD: enabling discovery in the era of big data}},
author = {Maria Pruzhinskaya and Emille E. O. Ishida and Konstantin Malanchev and Anastasia Lavrukhina and Etienne Russeil and Timofey Semenikhin and Sreevarsha Sreejith and Emmanuel Gangler and Matwey Kornilov and Vladimir Korolev and Alina Volnova},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.30477},
eprint = {2608.30477},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.30477},
}
RIS
TY - UNPB
TI - SNAD: enabling discovery in the era of big data
AU - Maria Pruzhinskaya
AU - Emille E. O. Ishida
AU - Konstantin Malanchev
AU - Anastasia Lavrukhina
AU - Etienne Russeil
AU - Timofey Semenikhin
AU - Sreevarsha Sreejith
AU - Emmanuel Gangler
AU - Matwey Kornilov
AU - Vladimir Korolev
AU - Alina Volnova
PY - 2026
DA - 2026/08/31
PB - arXiv
DO - 10.48550/arxiv.2608.30477
UR - https://arxiv.org/abs/2608.30477
AB - 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 objects and phenomena with machine learning (ML). The SNAD pipeline was built under the hypothesis that, although automatic ML algorithms have a crucial role to play in this task, the scientific discovery is only completely realized when such systems are designed to boost the impact of domain knowledge experts. Our key contributions include the development of the Coniferest Python library, which offers implementations of two active learning algorithms with an ``expert in loop'', and the creation of the SNAD Transient Miner, facilitating the search for specific types of transients. We have also developed the SNAD Viewer, a web portal that provides a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, making the analysis of potential anomalies more efficient. Finally, when applied to ZTF data, our approach has resulted in more than a hundred new supernova (SN) candidates, along with a few other non-catalogued objects, such as red dwarf flares, superluminous SNe, RS CVn type variables, and young stellar objects.
ER -