Exploring the Universe with SNAD: Anomaly Detection in Astronomy

Alina A. Volnova, Patrick D. Aleo, Anastasia Lavrukhina, Etienne Russeil, Timofey Semenikhin +7 more
10/24/2024
astro-ph.IMcs.HCcs.LG

No confident code implementation found for this paper.

Abstract

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. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of machine learning techniques within the field of astrophysics. This paper provides a review of the SNAD project and summarizes the advancements and achievements made by the team over several years.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

The weird and the wonderful in our Solar System: Searching for serendipity in the Legacy Survey of Space and Time

83% match2024

Building Machine Learning Challenges for Anomaly Detection in Science

83% match2025

AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning

82% match2025

Weakly-Supervised Anomaly Detection in the Milky Way

81% match2023

A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series

81% match1 repo32024

Statistical Machine Learning for Astronomy -- A Textbook

81% match1 repo642025

Cite this paper

@article{volnova2024exploring,
  title  = {Exploring the Universe with SNAD: Anomaly Detection in Astronomy},
  author = {Alina A. Volnova and Patrick D. Aleo and Anastasia Lavrukhina and Etienne Russeil and Timofey Semenikhin and Emmanuel Gangler and Emille E. O. Ishida and Matwey V. Kornilov and Vladimir Korolev and Konstantin Malanchev and Maria V. Pruzhinskaya and Sreevarsha Sreejith},
  year   = {2024},
  eprint = {2410.18875},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.18875v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2410.18875.svg)](https://codeofpaper.com/paper/2410.18875)

Discussion