The eROSITA Final Equatorial Depth Survey (eFEDS): the hard X-ray selected sample
Bibliographic record
Abstract
During its calibration and performance verification phase, the eROSITA instrument aboard the SRG satellite performed a uniform wide--area X-ray survey of approximately 140 deg$^{2}$ in a region of the sky known as the eROSITA Final Equatorial Depth Survey (eFEDS). The primary aim of eFEDS is to demonstrate the scientific performance to be expected at the end of the 8-pass eROSITA all sky survey. This will provide the first focussed image of the whole sky in the hard X-ray ($>2$~keV) bandpass. The expected source population in this energy range is thus of great interest, particularly for AGN studies. We use the 2.3--5 keV selection presented by Brunner et al. (2022) to construct a sample of 246 point-like hard X-ray sources for further study and characterization. These are classified as either extragalactic ($\sim 90$~\%) or Galactic ($\sim 10$~\%), with the former consisting overwhelmingly of AGN and the latter active stars. We concentrate our further analysis on the extragalactic/AGN sample, describing their X-ray and multiwavelength properties and comparing them to the eFEDS main AGN sample selected in the softer 0.2-2.3 keV band. The eROSITA hard band selects a subsample of sources that is a factor $>10$ brighter than the eFEDS main sample. The AGN within the hard population reach up to $z=3.2$ but on the whole are relatively nearby, with median $z$=0.34 compared to $z$=0.94 for the main sample. The hard survey probes typical luminosities in the range $\log L_{\rm X} = 43-46$. X-ray spectral analysis shows significant intrinsic absorption (with $\log N_{\rm H}>21$) in $\sim 20$~\% of the sources, with a hard X-ray power law continuum with mean $<Γ>=1.83\pm0.04$, typical of AGN, but slightly harder than the soft-selected eROSITA sample. (abridged)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".