Giant radio galaxies in the LOw-Frequency ARray Two-metre Sky Survey Boötes deep field
Bibliographic record
Abstract
ABSTRACT Giant radio galaxies (GRGs) are radio galaxies that have projected linear extents of more than 700 kpc or 1 Mpc, depending on definition. We have carried out a careful visual inspection in search of GRGs of the Boötes LOw-Frequency ARray Deep Field image at 150 MHz. We identified 74 GRGs with a projected size larger than 0.7 Mpc of which 38 are larger than 1 Mpc. The resulting GRG sky density is about 2.8 (1.43) GRGs per square degree for GRGs with linear size larger than 0.7 (1) Mpc. We studied their radio properties and the accretion state of the host galaxies using deep optical and infrared survey data and determined flux densities for these GRGs from available survey images at both 54 MHz and 1.4 GHz to obtain integrated radio spectral indices. We show the location of the GRGs in the P–D diagram. The accretion mode on to the central black holes of the GRG hosts is radiatively inefficient suggesting that the central engines are not undergoing massive accretion at the time of the emission. Interestingly, 14 out of 35 GRGs for which optical spectra are available show a moderate star-formation rate (10–100 $\rm M_{\odot }\,yr^{-1}$). Based on the number density of optical galaxies taken from the Dark Energy Spectroscopic Instrument survey DR9 photometric redshift catalogue, we found no significant differences between the environments of GRGs and other radio galaxies, at least for redshift up to z = 0.7.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".