XMMPZCAT: A catalogue of photometric redshifts for X-ray sources
Bibliographic record
Abstract
Aims. The third version of the XMM-Newton serendipitous catalogue (3XMM), containing almost half million sources, is now the largest X-ray catalogue. However, its full scientific potential remains untapped due to the lack of distance information (i.e. redshifts) for the majority of its sources. Here we present XMMPZCAT, a catalogue of photometric redshifts (photo-z) for 3XMM sources. Methods. We searched for optical counterparts of 3XMM-DR6 sources outside the Galactic plane in the SDSS and Pan-STARRS surveys, with the addition of near-(NIR) and mid-infrared (MIR) data whenever possible (2MASS, UKIDSS, VISTA-VHS, and AllWISE). We used this photometry data set in combination with a training sample of 5157 X-ray selected sources and the MLZ-TPZ package, a supervised machine learning algorithm based on decision trees and random forests for the calculation of photo-z. Results. We have estimated photo-z for 100 178 X-ray sources, about 50% of the total number of 3XMM sources (205 380) in the XMM-Newton fields selected to build this catalogue (4208 out of 9159). The accuracy of our results highly depends on the available photometric data, with a rate of outliers ranging from 4% for sources with data in the optical + NIR + MIR, up to ~40% for sources with only optical data. We also addressed the reliability level of our results by studying the shape of the photo-z probability density distributions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.029 |
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".