THE HARD X-RAY PERSPECTIVE ON THE SOFT X-RAY EXCESS
Bibliographic record
Abstract
The X-ray spectra of many active galactic nuclei exhibit a "soft excess" below 1 keV, whose physical origin remains unclear. Diverse models have been suggested to account for it, including ionized reflection of X-rays from the inner part of the accretion disk, ionized winds/absorbers, and Comptonization. The ionized reflection model suggests a natural link between the prominence of the soft excess and the Compton reflection hump strength above 10 keV, but it has not been clear what hard X-ray signatures, if any, are expected from the other soft X-ray candidate models. Additionally, it has not been possible up until recently to obtain high-quality simultaneous measurements of both soft and hard X-ray emission necessary to distinguish these models but upcoming joint XMM–NuSTAR programs provide precisely this opportunity. In this paper, we present an extensive analysis of simulations of XMM–NuSTAR observations, using two candidate soft excess models as inputs, to determine whether such campaigns can disambiguate between them by using hard and soft X-ray observations in tandem. The simulated spectra are fit with the simplest "observer's model" of a blackbody and neutral reflection to characterize the strength of the soft and hard excesses. A plot of the strength of the hard excess against the soft excess strength provides a diagnostic plot which allows the soft excess production mechanism to be determined in individual sources and samples using current state-of-the-art and next generation hard X-ray enabled observatories. This approach can be straightforwardly extended to other candidate models for the soft excess.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".