Spurious One-Month and One-Year Periods in Visual Observations of Variable Stars
Bibliographic record
Abstract
Visual observations of variable stars, when time-series analyzed with some algorithms such as DC-DFT in vstar, show spurious periods at or close to one synodic month (29.5306 days), and also at about a year, with an amplitude of typically a few hundredths of a magnitude. The one-year periods have been attributed to the Ceraski effect, which was believed to be a physiological effect of the visual observing process. This paper reports on time-series analysis, using DC-DFT in vstar, of visual observations (and in some cases, V observations) of a large number of stars in the AAVSO International Database, initially to investigate the one-month periods. The results suggest that both the one-month and one-year periods are actually due to aliasing of the stars' very low-frequency variations, though they do not rule out very low-amplitude signals (typically 0.01 to 0.02 magnitude) which may be due to a different process, such as a physiological one. Most or all of these aliasing effects may be avoided by using a different algorithm, which takes explicit account of the window function of the data, and/or by being fully aware of the possible presence of and aliasing by very low-frequency variations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".