Yarin Gal, Shreshth Malik, Nora Eisner, Chris Lintott
We lifted 10 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| s-a-malik/pht-ml | canonical | 9 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| bce_loss_numpy | Ran | s-a-malik/pht-ml/src/utils/utils.py code served (permissive licence) · get_code("3ca1e1df3fe3f3f6") |
| compute_normalization_fixed_point | Ran | s-a-malik/pht-ml/src/models/bi_tempered_loss.py code served (permissive licence) · get_code("5073d70103d112db") |
| exp_t | Ran | s-a-malik/pht-ml/src/models/bi_tempered_loss.py code served (permissive licence) · get_code("0bc2e58b7e4855cc") |
| get_activation | Ran | s-a-malik/pht-ml/src/models/components.py code served (permissive licence) · get_code("a541b8104eeed749") |
| get_lc_file | Ran | s-a-malik/pht-ml/src/utils/plot_lc.py code served (permissive licence) · get_code("7ca526c10f52d529") |
| get_sectors | Ran | s-a-malik/pht-ml/src/utils/utils.py code served (permissive licence) · get_code("4f414829b43bcb9f") |
| log_t | Ran | s-a-malik/pht-ml/src/models/bi_tempered_loss.py code served (permissive licence) · get_code("cb10af3f4a51397e") |
| rebin | Ran | s-a-malik/pht-ml/src/utils/plot_lc.py code served (permissive licence) · get_code("811e5e29112fd128") |
| rebin | Ran | s-a-malik/pht-ml/src/utils/preprocess_lcs.py code served (permissive licence) · get_code("24c6591917c6eaf2") |
| load_checkpoint | Not yet run | s-a-malik/pht-ml/src/utils/utils.py code served (permissive licence) · get_code("cf4041c7c0310f38") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Automated planetary transit detection has become vital to prioritize candidates for expert analysis given the scale of modern telescopic surveys. While current methods for short-period exoplanet detection work effectively due to periodicity in the light curves, there lacks a robust approach for detecting single-transit events. However, volunteer-labelled transits recently collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a datadriven approach to long-period exoplanet detection. In this work, we train a 1-D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find using volunteer scores significantly improves performance over synthetic data, and enables the recovery of known planets at a precision and rate matching that of the volunteers. Importantly, the model also recovers transits found by volunteers but missed by current automated methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.06903")
get_code_for_paper("2211.06903")
have("2211.06903")
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