The LOFAR Two-metre Sky Survey
Bibliographic record
Abstract
The LOFAR Two-metre Sky Survey (LoTSS) is a sensitive, high-resolution 120–168 MHz survey of the Northern sky. The LoTSS First Data Release (DR1) presents 424 square degrees of radio continuum observations over the HETDEX Spring Field (10h45m00s < right ascension < 15h30m00s and 45°00′00″ < declination < 57°00′00″) with a median sensitivity of 71 μJy beam−1 and a resolution of 6″. In this paper we present photometric redshifts (photo-z) for 94.4% of optical sources over this region that are detected in the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) 3π steradian survey. Combining the Pan-STARRS optical data with mid-infrared photometry from the Wide-field Infrared Survey Explorer, we estimate photo-zs using a novel hybrid photometric redshift methodology optimised to produce the best possible performance for the diverse sample of radio continuum selected sources. For the radio-continuum detected population, we find an overall scatter in the photo-z of 3.9% and an outlier fraction (|zphot−zspec|/(1 + zspec) > 0.15) of 7.9%. We also find that, at a given redshift, there is no strong trend in photo-z quality as a function of radio luminosity. However there are strong trends as a function of redshift for a given radio luminosity, a result of selection effects in the spectroscopic sample and/or intrinsic evolution within the radio source population. Additionally, for the sample of sources in the LoTSS First Data Release with optical counterparts, we present rest-frame optical and mid-infrared magnitudes based on template fits to the consensus photometric (or spectroscopic when available) redshift.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".