Spectral signatures of ultra-rapidly varying objects in spectroscopic surveys
Bibliographic record
Abstract
Context. Astronomy is an observationally-led subject where chance discoveries play an important role. The time domain is the least explored in astronomy.Aims. We alert spectroscopists, particularly those involved in surveys, that rapidly varying sources can induce periodic patterns in spectra. Methods. The analytical treatment is based on standard Fourier theory. It is used to predict the shapes of features introduced by intensity pulses in spectra. These are the shapes that one must look for in spectral surveys. The theoretical analysis is supported by published experiments that measured the spectral modulation, in the visible wavelength region, caused by pairs of 150 femtosecond laser pulses separated by time periods varying between 5 10-13 s and 3 10-11 s. Results. Detection of spectral signatures would allow the detection of new classes of objects that emit bursts of pulses separated by time intervals that are too short to be detected with conventional techniques. The principal advantage of the technique is that there is no need for specialized instruments or surveys: one must only incorporate algorithms capable of searching for periodic spectroscopic signals, such as those shown in a figure in this article, into existing data analyzis software and use it with standard spectroscopic surveys (including existing ones).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".