Quasar Island -three new z ∼6 quasars, including a lensed candidate, identified with contrasti v e learning

Abstract

Of the hundreds of z ≳6 quasars disco v ered 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 surv e ys by applying strict photometric selection criteria, in particular by imposing non-detections in bands blueward of the Lyman- αline. Such procedures by design prohibit the disco v ery of lensed quasars, as the lensing foreground galaxy would contaminate the photometry of the quasar. We present a no v el quasar selection methodology, applying contrastive learning (an unsupervised machine learning technique) to Dark Energy Surv e y 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 disco v ered 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.