The optical luminosity function of LOFAR radio-selected quasars at 1.4 ≤<i>z</i>≤ 5.0 in the NDWFS-Boötes field
Bibliographic record
Abstract
We present an estimate of the optical luminosity function (OLF) of LOFAR radio-selected quasars (RSQs) at 1.4 < z < 5.0 in the 9.3 deg2NOAO Deep Wide-field survey (NDWFS) of the Boötes field. The selection was based on optical and mid-infrared photometry used to train three different machine learning (ML) algorithms (Random forest, SVM, Bootstrap aggregation). Objects taken as quasars by the ML algorithms are required to be detected at ≥5σsignificance in deep radio maps to be classified as candidate quasars. The optical imaging came from the Sloan Digital Sky Survey and the Pan-STARRS1 3πsurvey; mid-infrared photometry was taken from theSpitzerDeep, Wide-Field Survey; and radio data was obtained from deep LOFAR imaging of the NDWFS-Boötes field. The requirement of a 5σLOFAR detection allowed us to reduce the stellar contamination in our sample by two orders of magnitude. The sample comprises 130 objects, including both photometrically selected candidate quasars (47) and spectroscopically confirmed quasars (83). The spectral energy distributions calculated using deep photometry available for the NDWFS-Boötes field confirm the validity of the photometrically selected quasars using the ML algorithms as robust candidate quasars. The depth of our LOFAR observations allowed us to detect the radio-emission of quasars that would be otherwise classified as radio-quiet. Around 65% of the quasars in the sample are fainter thanM1450 = −24.0, a regime where the OLF of quasars selected through their radio emission, has not been investigated in detail. It has been demonstrated that in cases where mid-infrared wedge-based AGN selection is not possible due to a lack of appropriate data, the selection of quasars using ML algorithms trained with optical and infrared photometry in combination with LOFAR data provides an excellent approach for obtaining samples of quasars. The OLF of RSQs can be described by pure luminosity evolution atz < 2.4, and a combined luminosity and density evolution atz > 2.4. The faint-end slope,α, becomes steeper with increasing redshift. This trend is consistent with previous studies of faint quasars (M1450 ≤ −22.0). We demonstrate that RSQs show an evolution that is very similar to that exhibited by faint quasars. By comparing the spatial density of RSQs with that of the total (radio-detected plus radio-undetected) faint quasar population at similar redshifts, we find that RSQs may compose up to ∼20% of the whole faint quasar population. This fraction, within uncertainties, is constant with redshift. Finally, we discuss how the compactness of the RSQs radio-morphologies and their steep spectral indices could provide valuable insights into how quasar and radio activity are triggered in these systems.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".