THE CHANDRA MULTI-WAVELENGTH PROJECT: OPTICAL SPECTROSCOPY AND THE BROADBAND SPECTRAL ENERGY DISTRIBUTIONS OF X-RAY-SELECTED AGNs
Bibliographic record
Abstract
From optical spectroscopy of X-ray sources observed as part of ChaMP, we present redshifts and classifications for a total of 1569 Chandra sources from our targeted spectroscopic follow up using the FLWO, SAAO, WIYN, CTIO, KPNO, Magellan, MMT and Gemini telescopes, and from archival SDSS spectroscopy. We classify the optical counterparts as 50% BLAGN, 16% NELG, 14% ALG, and 20% stars. We detect QSOs out to z~5.5 and galaxies out to z~3. We have compiled extensive photometry from X-ray to radio bands. Together with our spectroscopic information, this enables us to derive detailed SEDs for our extragalactic sources. We fit a variety of templates to determine bolometric luminosities, and to constrain AGN and starburst components where both are present. While ~58% of X-ray Seyferts require a starburst event to fit observed photometry only 26% of the X-ray QSO population appear to have some kind of star formation contribution. This is significantly lower than for the Seyferts, especially if we take into account torus contamination at z>1 where the majority of our X-ray QSOs lie. In addition, we observe a rapid drop of the percentage of starburst contribution as X-ray luminosity increases. This is consistent with the quenching of star formation by powerful QSOs, as predicted by the merger model, or with a time lag between the peak of star formation and QSO activity. We have tested the hypothesis that there should be a strong connection between X-ray obscuration and star-formation but we do not find any association between X-ray column density and star formation rate both in the general population or the star-forming X-ray Seyferts. Our large compilation also allows us to report here the identification of 81 XBONG, 78 z>3 X-ray sources and 8 Type-2 QSO candidates. Also we have identified the highest redshift (z=5.4135) X-ray selected QSO with optical spectroscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".