Impact of detector calibration accuracy on black hole spectroscopy
Bibliographic record
Abstract
Systematic errors in detector calibration can bias signal analyses and potentially lead to incorrect interpretations suggesting violations of general relativity. In this study, we investigate how calibration systematics affect black hole (BH) spectroscopy, a technique that uses the quasinormal modes (QNMs) emitted during the ringdown phase of gravitational waves to study remnant BHs formed in compact binary coalescences. We simulate a series of physically motivated, tunable calibration errors and use them to intentionally miscalibrate numerical relativity waveforms. We then apply a QNM extraction method---the rational QNM filter---to quantify the impact of these calibration errors. We find that current calibration standards (errors within 10% in magnitude and 10\ifmmode^\circ\else\textdegree\fi{} in phase across the most sensitive frequency range of 20--2000 Hz) are adequate for BH ringdown analyses with existing observations, but insufficient for the accuracy goals of future upgraded and next-generation observatories. Specifically, we show that for events with a high ringdown signal-to-noise ratio of $\ensuremath{\sim}120$, calibration errors must remain $\ensuremath{\lesssim}4%$ in magnitude and $\ensuremath{\lesssim}4\ifmmode^\circ\else\textdegree\fi{}$ in phase to avoid introducing biases. While this analysis focuses on a particular aspect of BH spectroscopy, the results offer quantitative benchmarks for calibration standards crucial to fully realize the potential of precision tests of general relativity in the next-generation detector era.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".