Quasar Island -- Three new z~6 quasars, including a lensed candidate, identified with contrastive learning

Of the hundreds of z6 quasars discovered to date, only one is known to be gravitationally lensed, despite the high lensing optical depth expected at z6. 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 7 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 Gemini-South/GMOS and archival NTT/EFOSC2 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.