We lifted 8 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| uiucsn/elasticc-classification | canonical | 8 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| augment_ts_length_to_days_since_trigger | Ran | uiucsn/elasticc-classification/src/astroOracle/dataloader.py code served (permissive licence) · get_code("f54f3d28d7a8e2ab") |
| get_highest_prob_path | Ran | uiucsn/elasticc-classification/src/astroOracle/taxonomy.py code served (permissive licence) · get_code("12c54735848970f0") |
| get_indices_where | Ran | uiucsn/elasticc-classification/src/astroOracle/interpret_results.py code served (permissive licence) · get_code("a5f69fc132694f5c") |
| get_most_likely_path | Ran | uiucsn/elasticc-classification/src/astroOracle/taxonomy.py code served (permissive licence) · get_code("b70b77286087b599") |
| get_prediction_probs | Ran | uiucsn/elasticc-classification/src/astroOracle/taxonomy.py code served (permissive licence) · get_code("a69c4160ce8a2773") |
| get_ts_upto_days_since_trigger | Ran | uiucsn/elasticc-classification/src/astroOracle/dataloader.py code served (permissive licence) · get_code("77cc4733c4218c23") |
| load | Ran | uiucsn/elasticc-classification/src/astroOracle/dataloader.py code served (permissive licence) · get_code("f53c76364c62b293") |
| parse_args | Ran | uiucsn/elasticc-classification/src/astroOracle/class_summaries.py code served (permissive licence) · get_code("10982aae3770c25a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2501.01496")
get_code_for_paper("2501.01496")
have("2501.01496")
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