Application of the trend filtering algorithm to the MACHO database
Bibliographic record
Abstract
Aims. Because of the strong effect of systematics (or trends) in variable star observations, we apply the Trend Filtering Algorithm (TFA) to a subset of the MACHO database and search for variable stars.Methods. TFA has been applied successfully in planetary transit searches, where weak, short-duration periodic dimmings are sought in the presence of noise and various systematics (due to, e.g., imperfect flat-fielding, crowding, etc.). These latter effects introduce colored noise in the photometric time-series that can completely overwhelm the signal. By using a large number of available photometric time-series of a given field, TFA utilizes the fact that the same types of systematics appear in several or many time-series of the same field. As a result, we attempt to reproduce each target time-series by a linear combination of templates, optimized by least-squares. After a signal has been identified in the residuals between the original time-series and the systematics computed by TFA, we reconstruct the signal by employing the full model, including the signal, systematics, and noise.Results. We apply TFA to the brightest ~5300 objects from subsets of each of the MACHO Large Magellanic Cloud fields #1 and #79. We find that the Fourier frequency analysis performed on the original data detect some 60% of the objects as trend-dominated. This figure decreases to almost zero after using TFA. In total, we detect 387 variables in the two fields, 183 of which would have remained undetected without using TFA. Where possible, we provide preliminary classification of the variables found.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".