Measuring key X‐ray parameters from simulated spectra of Seyfert galaxies
Bibliographic record
Abstract
Abstract Supermassive black hole (SMBH) accretion is an important topic of astro‐physical research for a variety of fields. However, literature shows that precisely modelling the X‐ray reflection component of an accretion disk around a SMBH is challenging. We test how reliably we can measure key active galactic nuclei (AGN) X‐ray reflection parameters by simulating the spectra of average Seyfert 1 galaxies and then fitting those spectra using common modelling techniques. The AGN spectra were created from 0.01–300.0 keV using XMM‐Newton pn responses and with (3.75±0.10)×106 counts in the 2–10 keV band. The model RELXILL was used to create all spectra with a reflection fraction (ratio of reflected flux over primary flux from 0.1–100 keV) of R = 1. The values of six key parameters were generated randomly within given ranges to simulate the spectra: photon index (Γ), inner emissivity index (q 1), black hole spin (a), disk inclination angle (θ), ionization (ξ), and iron abundance in solar units (A Fe). Once the simulated spectra were created, they were autonomously fit with RELXILL from 2.5–10.0 keV. All six key parameters were allowed to vary throughout the modelling process, with a gradual thawing of model parameters to mimic manual fitting procedure. We find that we can successfully measure key AGN spectral components in this scenario, but some conditions apply: Γ, θ, and A Fe are well constrained, however they are also consistently overestimated. Only black hole spin (a) measurements that are in extrema, a > 0.9, can be considered reliable. Lastly, ξ and q 1 cannot be measured and these parameters – along with intermediate spin values – should be verified using other techniques. (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".