The LOFAR Two-meter Sky Survey: Deep Fields Data Release 1
Bibliographic record
Abstract
The Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) is a sensitive, high-resolution 120-168 MHz survey split across multiple tiers over the northern sky. The first LoTSS Deep Fields data release consists of deep radio continuum imaging at 150 MHz of the Boötes, European Large Area Infrared Space Observatory Survey-North 1, and Lockman Hole fields, down to rms sensitivities of ~32, 20, and 22 μ Jy beam −1 , respectively. In this paper we present consistent photometric redshift (photo- z ) estimates for the optical source catalogues in all three fields – totalling over 7 million sources (~5 million after limiting to regions with the best photometric coverage). Our photo- z estimation uses a hybrid methodology that combines template fitting and machine learning and is optimised to produce the best possible performance for the radio continuum selected sources and the wider optical source population. Comparing our results with spectroscopic redshift samples, we find a robust scatter ranging from 1.6 to 2% for galaxies and 6.4 to 7% for identified optical, infrared, or X-ray selected active galactic nuclei. Our estimated outlier fractions (| z phot − z spec |/(1+ z spec )>0.15) for the corresponding subsets range from 1.5 to 1.8% and 18 to 22%, respectively. Replicating trends seen in analyses of previous wide-area radio surveys, we find no strong trend in photo- z quality as a function of radio luminosity for a fixed redshift. We exploit the broad wavelength coverage available within each field to produce galaxy stellar mass estimates for all optical sources at z < 1.5. Stellar mass functions derived for each field are used to validate our mass estimates, with the resulting estimates in good agreement between each field and with published results from the literature.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".