The high energy X-ray probe (HEX-P): the future of hard X-ray dual AGN science
Bibliographic record
Abstract
A fundamental goal of modern-day astrophysics is to understand the connection between supermassive black hole (SMBH) growth and galaxy evolution. Merging galaxies offer one of the most dramatic channels for galaxy evolution known, capable of driving inflows of gas into galactic nuclei, potentially fueling both star formation and central SMBH activity. Dual active galactic nuclei (dual AGNs) in late-stage mergers with nuclear pair separations <10 kpc are thus ideal candidates to study SMBH growth along the merger sequence since they coincide with the most transformative period for galaxies. However, dual AGNs can be extremely difficult to confirm and study. Hard X-ray ( >10 keV) studies offer a relatively contamination-free tool for probing the dense obscuring environments predicted to surround the majority of dual AGN in late-stage mergers. To date, only a handful of the brightest and closest systems have been studied at these energies due to the demanding instrumental requirements involved. We demonstrate the unique capabilities of HEX-P to spatially resolve the soft and - for the first time - hard X-ray counterparts of closely-separated (∼2″−5″) dual AGNs in the local Universe. By incorporating physically-motivated obscuration models, we reproduce realistic broadband X-ray spectra expected for deeply embedded accreting SMBHs. Hard X-ray spatially resolved observations of dual AGNs—accessible only to HEX-P—will hence transform our understanding of dual AGN in the nearby Universe.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".