ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914177954611200 |
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| author | Shah, Ved G. Gagliano, Alex Malanchev, Konstantin Narayan, Gautham Malz, Alex I. Collaboration, The LSST Dark Energy Science |
| author_facet | Shah, Ved G. Gagliano, Alex Malanchev, Konstantin Narayan, Gautham Malz, Alex I. Collaboration, The LSST Dark Energy Science |
| contents | We present 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 (GRUs), 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 $\sim$0.5M events from the Extended LSST Astronomical Time-Series Classification Challenge, we achieve a top-level (Transient vs Variable) macro-averaged 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 sub-types, 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 (https://github.com/uiucsn/ELAsTiCC-Classification). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST Shah, Ved G. Gagliano, Alex Malanchev, Konstantin Narayan, Gautham Malz, Alex I. Collaboration, The LSST Dark Energy Science Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Artificial Intelligence Machine Learning We present 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 (GRUs), 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 $\sim$0.5M events from the Extended LSST Astronomical Time-Series Classification Challenge, we achieve a top-level (Transient vs Variable) macro-averaged 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 sub-types, 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 (https://github.com/uiucsn/ELAsTiCC-Classification). |
| title | ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.01496 |