Bibliographic record
Abstract
Being one of only two fundamental properties black holes possess, the spin of supermassive black holes (SMBHs) is of great interest for understanding accretion processes and galaxy evolution. However, in these early days of spin measurements, consistency and reproducibility of spin constraints have been a challenge. Here, we focus on X-ray spectral modelling of active galactic nuclei (AGN), examining how well we can truly return known reflection parameters such as spin under standard conditions. We have created and fit over 4000 simulated Seyfert 1 spectra each with 375±1k counts. We assess the fits with reflection fraction of R = 1 as well as reflection-dominated AGN with R = 5. We also examine the consequence of permitting fits to search for retrograde spin. In general, we discover that most parameters are overestimated when spectroscopy is restricted to the 2.5–10.0 keV regime and that models are insensitive to inner emissivity index and ionization. When the bandpass is extended out to 70 keV, parameters are more accurately estimated. Repeating the process for R = 5 reduces our ability to measure photon index (∼3 to 8 per cent error and overestimated), but increases precision in all other parameters – most notably ionization, which becomes better constrained (±45|${\rm \thinspace erg}{\rm \thinspace cm}{\rm \thinspace s}^{-1}$|) for low-ionization parameters (ξ < 200|${\rm \thinspace erg}{\rm \thinspace cm}{\rm \thinspace s}^{-1}$|). In all cases, we find the spin parameter is only well measured for the most rapidly rotating SMBHs (i.e. a > 0.8 to about ±0.10) and that inner emissivity index is never well constrained. Allowing our model to search for retrograde spin did not improve the results.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".