| Title: |
ORACLE: A Real-time, Hierarchical, Deep Learning Photometric Classifier for the LSST |
| Authors: |
Ved G. Shah; Alex Gagliano; Konstantin Malanchev; Gautham Narayan; Alex I. Malz; and the LSST Dark Energy Science Collaboration |
| Source: |
The Astrophysical Journal, Vol 995, Iss 1, p 4 (2025) |
| Publisher Information: |
IOP Publishing |
| Publication Year: |
2025 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
Astroinformatics; High energy astrophysics; Astrostatistics; Supernovae; Classification; Stellar classification; Astrophysics; QB460-466 |
| Description: |
We present the Online Ranked Astrophysical CLass Estimator (ORACLE), the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with gated recurrent units, and has been trained using a custom hierarchical cross-entropy loss function to provide high-confidence classifications along an observationally driven taxonomy with as little as a single photometric observation. Contextual information for each object, including host galaxy photometric redshift, offset, ellipticity, and brightness, is concatenated to the light-curve embedding and used to make a final prediction. Training on ∼0.5M events from the Extended LSST Astronomical Time-series Classification Challenge, we achieve a top-level (transient versus variable) macroaveraged precision of 0.96 using only 1 day of photometric observations after the first detection in addition to contextual information, for each event; this increases to >0.99 once 64 days of the light curve has been obtained, and 0.83 at 1024 days after first detection for 19-way classification (including supernova subtypes, active galactic nuclei, variable stars, microlensing events, and kilonovae). We also compare ORACLE with other state-of-the-art classifiers and report comparable performance for the 19-way classification task, in addition to delivering accurate top-level classifications much earlier. The code and model weights used in this work are publicly available at our associated GitHub repository ( http://github.com/uiucsn/Astro-ORACLE ). |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://doi.org/10.3847/1538-4357/ae1130; https://doaj.org/toc/1538-4357; https://doaj.org/article/36b466c4da064ead9fb31eaa39cd7aba |
| DOI: |
10.3847/1538-4357/ae1130 |
| Availability: |
https://doi.org/10.3847/1538-4357/ae1130; https://doaj.org/article/36b466c4da064ead9fb31eaa39cd7aba |
| Accession Number: |
edsbas.F6A05E99 |
| Database: |
BASE |