On the Classification and Feature Relevance of Multiband Light Curves
Why this work is in the frame
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Bibliographic record
Abstract
Abstract With an expected torrent of data from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the need for automated identification of noisy and sparse light curves will increase drastically. In this paper, we performed classification of multiband astronomical light curves from the Photometric LSST Astronomical Time-series Classification Challenge ( PLAsTiCC ) data set via boosted neural nets, boosted decision trees, and a voted classifier for 14 astronomical categories. In order to deal with noisy features, we used wavelet decomposition together with feature selection. We also performed a feature ranking method using a neural network. Our method may be considered an alternative to random forests, which is known to favor features with more categories as relevant. We also investigated the class importance with neural nets using a one-versus-all approach which reduces the multiclass problem to a binary class problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it