Timeline analysis and wavelet multiscale analysis of the AKARI All-Sky Survey at 90 μm
Bibliographic record
Abstract
We present a careful analysis of the point-source detection limit of the AKARI All-Sky Survey in the WIDE-S 90-μm band near the North Ecliptic Pole (NEP). Timeline analysis is used to detect IRAS (Infrared Astronomy Satellite) sources and then a conversion factor is derived to transform the peak timeline signal to the interpolated 90-μm flux of a source. Combined with a robust noise measurement, the point-source flux detection limit at signal-to-noise ratio (S/N) > 5 for a single detector row is 1.1 ± 0.1 Jy which corresponds to a point-source detection limit of the survey of ∼0.4 Jy. Wavelet transform offers a multiscale representation of the Time Series Data (tsd). We calculate the continuous wavelet transform of the tsd and then search for significant wavelet coefficients considered as potential source detections. To discriminate real sources from spurious or moving objects, only sources with confirmation are selected. In our multiscale analysis, IRAS sources selected above 4σ can be identified as the only real sources at the Point Source Scales. We also investigate the correlation between the non-IRAS sources detected in timeline analysis and cirrus emission using wavelet transform and contour plots of wavelet power spectrum. It is shown that the non-IRAS sources are most likely to be caused by excessive noise over a large range of spatial scales rather than real extended structures such as cirrus clouds.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".