Exploring the Universe with SNAD: Anomaly Detection in Astronomy
arXiv:2410.18875 · doi:10.1007/978-3-031-67826-4_15
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.
14 pages, 4 figures
References in corpus (21)
- The Zwicky Transient Facility: System Overview, Performance, and First Results
- Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
- An Open Catalog for Supernova Data
- SNANA: A Public Software Package for Supernova Analysis
- The Zwicky Transient Facility Catalog of Periodic Variable Stars
- The Most Luminous Supernovae
- Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
- Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
- The weirdest SDSS galaxies: results from an outlier detection algorithm
- Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning
- Anomaly detection in the Zwicky Transient Facility DR3
- Active Anomaly Detection for time-domain discoveries
- Anomaly Detection in the Open Supernova Catalog
- Incorporating Feedback into Tree-based Anomaly Detection
- Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks
- The SNAD Viewer: Everything You Want to Know about Your Favorite ZTF Object
- Supernova search with active learning in ZTF DR3
- SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility
- SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees
- Rubin Observatory LSST Transients and Variable Stars Roadmap
- Multi-View Symbolic Regression