Applying wavelet analysis to the X-ray light curves of active galactic nuclei and quasi-periodic eruptions
Bibliographic record
Abstract
ABSTRACT In this work, we examine the application of the wavelet transform to the X-ray timing analyses of active galactic nuclei (AGN) and quasi-periodic eruption sources (QPEs). Several scenarios are simulated to test the effectiveness of the wavelet analysis to stationary and non-stationary data. We find that the power spectral density (PSD) slope and the nature of the periodic signal can influence the ability to identify important features in the wavelet power spectrum. In general, weak and transient features can be discerned, which make the wavelet spectrum an important tool in examining AGN light curves. We carried out a wavelet analysis to four unique objects: Ark 120, IRAS 13224-3809, RE J1034+396, and the QPE GSN 069. The well-known quasi-periodic oscillation (QPO) in RE J1034 + 396 is significantly detected in the wavelet power spectrum. In IRAS 13224-3809, significant transient features appear during a flare at frequencies coincident with previously detected reverberation signals. Finally, the wavelet power spectrum of the QPE GSN 069 significantly reveals four persistent signals that exhibit a 3:2 ratio in oscillation frequencies, consistent with high-frequency QPOs in stellar mass X-ray binaries, but we cannot rule out the possibility this is an artefact of the calculation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".