# Exploring the Universe with SNAD: Anomaly Detection in Astronomy

arXiv: https://arxiv.org/abs/2410.18875 · HTML: https://mlsanity.com/paper/2410.18875/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)
- [Active Anomaly Detection for time-domain discoveries](https://mlsanity.com/paper/1909.13260/) (2019)
- [DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection](https://mlsanity.com/paper/2302.02005/) (2023)
- [Incorporating Feedback into Tree-based Anomaly Detection](https://mlsanity.com/paper/1708.09441/) (2017)

## Built on by (most cited first, 1 of 1)

- [In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys](https://mlsanity.com/paper/2510.23702/) (2025)

## Abstract (arXiv)

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.