Quasar Island – Three new z∼6 quasars, including a lensed candidate, identified with contrastive learning
Monthly Notices of the Royal Astronomical Society(2024)
摘要
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 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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要