<i>AEGIS-X:</i> THE <i>CHANDRA</i> DEEP SURVEY OF THE EXTENDED GROTH STRIP
Bibliographic record
Abstract
We present the AEGIS-X survey, a series of deep Chandra ACIS-I observations of the Extended Groth Strip. The survey comprises pointings at eight separate positions, each with nominal exposure of 200 ks, covering a total area of approximately 0.67 deg 2 in a strip of length 2 degrees. We describe in detail an updated version of our data reduction and point-source-detection algorithms used to analyze these data. A total of 1325 band-merged sources have been found to a Poisson probability limit of 4 × 10 −6 , with limiting fluxes of 5.3 × 10 −17 erg cm 2 s −1 in the soft (0.5–2 keV) band and 3.8 × 10 −16 erg cm −2 s −1 in the hard (2–10 keV) band. We present simulations verifying the validity of our source-detection procedure and showing a very small, <1.5%, contamination rate from spurious sources. Optical/NIR counterparts have been identified from the DEEP2, CFHTLS, and Spitzer /Infrared Array Camera (IRAC) surveys of the same region. Using a likelihood ratio method, we find optical counterparts for 76% of our sources, complete to R AB = 24.1, and, of the 66% of the sources that have IRAC coverage, 94% have a counterpart to a limit of 0.9 μJy at 3.6 μm ( m AB = 23.8). After accounting for (small) positional offsets in the eight Chandra fields, the astrometric accuracy of Chandra positions is found to be 0 8 rms; however, this number depends both on the off-axis angle and the number of detected counts for a given source. All data products described in this paper are made available via a public Web site.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".