A PUBLIC, <i>K</i> -SELECTED, OPTICAL-TO-NEAR-INFRARED CATALOG OF THE EXTENDED CHANDRA DEEP FIELD SOUTH (ECDFS) FROM THE MULTIWAVELENGTH SURVEY BY YALE-CHILE (MUSYC)
Bibliographic record
Abstract
We present a new, K -selected, optical-to-near infrared photometric catalog of the Extended Chandra Deep Field South (ECDFS), making it publicly available to the astronomical community. 22 The data set is founded on publicly available imaging, supplemented by original z ' JK imaging data collected as part of the MUltiwavelength Survey by Yale–Chile (MUSYC). The final photometric catalog consists of photometry derived from UU 38 BVRIz ' JK imaging covering the full of the ECDFS, plus H -band photometry for approximately 80% of the field. The 5σ flux limit for point sources is . This is also the nominal completeness and reliability limit of the catalog: the empirical completeness for 21.75 < K < 22.00 is ≳85%. We have verified the quality of the catalog through both internal consistency checks, and comparisons to other existing and publicly available catalogs. As well as the photometric catalog, we also present catalogs of photometric redshifts and rest-frame photometry derived from the 10-band photometry. We have collected robust spectroscopic redshift determinations from published sources for 1966 galaxies in the catalog. Based on these sources, we have achieved a (1σ) photometric redshift accuracy of Δ z /(1 + z ) = 0.036, with an outlier fraction of 7.8%. Most of these outliers are X-ray sources. Finally, we describe and release a utility for interpolating rest-frame photometry from observed spectral energy distributions, dubbed InterRest. 23
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.028 |
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".