<i>Quasar Island</i> – three new <i>z</i> ∼ 6 quasars, including a lensed candidate, identified with contrastive learning
Bibliographic record
Abstract
ABSTRACT Of the hundreds of z ≳ 6 quasars discovered to date, only one is known to be gravitationally lensed, despite the high lensing optical depth expected at z ≳ 6. High-redshift quasars are typically identified in large-scale surveys by applying strict photometric selection criteria, in particular by imposing non-detections in bands blueward of the Lyman-α line. Such procedures by design prohibit the discovery of lensed quasars, as the lensing foreground galaxy would contaminate the photometry of the quasar. We present a novel quasar selection methodology, applying contrastive learning (an unsupervised machine learning technique) to Dark Energy Survey imaging data. We describe the use of this technique to train a neural network which isolates an ‘island’ of 11 sources, of which seven are known z ∼ 6 quasars. Of the remaining four, three are newly discovered quasars (J0109−5424, z = 6.07; J0122−4609, z = 5.99; J0603−3923, z = 5.94), as confirmed by follow-up and archival spectroscopy, implying a 91 per cent efficiency for our novel selection method; the final object on the island is a brown dwarf. In one case (J0109−5424), emission below the Lyman limit unambiguously indicates the presence of a foreground source, though high-resolution optical/near-infrared imaging is still needed to confirm the quasar’s lensed (multiply imaged) nature. Detection in the g band has led this quasar to escape selection by traditional colour cuts. Our findings demonstrate that machine learning techniques can thus play a key role in unveiling populations of quasars missed by traditional methods.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".