16 new quasars at the end of the reionization unveiled by self-supervised learning

TL;DR

This study employs self-supervised learning on optical survey data to efficiently identify high-redshift quasars, discovering 16 new quasars at z > 6 with properties that challenge traditional selection methods.

Contribution

The paper introduces a novel self-supervised machine learning approach combined with SED fitting for high-redshift quasar detection, outperforming traditional color-based methods.

Findings

Discovered 16 new quasars at z = 5.94-6.45 with high success rate

Identified quasars with unusual properties missed by traditional methods

Demonstrated scalability of the approach for future wide-field surveys

Abstract

Luminous quasars at $z > 6$ are key probes of early supermassive black hole (SMBH) growth, massive galaxy evolution, and intergalactic medium properties during cosmic reionization. However, their discovery is very challenging due to their scarcity and overwhelming contamination, as foreground ultracool dwarfs (UCDs) outnumber $z>6$ quasars by 2-4 orders of magnitude. In this work, we leverage the extensive coverage of DESI Legacy Survey DR10 to conduct a self-supervised search for quasars at $z > 6$, directly analyzing multiband optical images and minimizing the biases of traditional catalog-driven color-color selection criteria. By applying a contrastive learning (CL) method followed by spectral energy distribution (SED) fitting prioritization, we identified 1139 high-priority quasar candidates, for which we expect a competitive $\sim$1:1 quasar-to-UCD ratio based on literature samples. We spectroscopically confirm 16 new quasars at $z = 5.94$-6.45, achieving a 45\% success rate. Remarkably, all 16 objects are relatively bright ($M_{1450} < -25.5$) quasars, including several with unusual properties such as narrow Ly$\alpha$ emission (FWHM $< 2600$ km s$^{-1}$), strong Ly$\alpha$+NV emission with equivalent width $>100$ \AA, and mildly red observed-frame near-infrared (NIR) continua ($z - J > 0.4$). Notably, three of them would have been missed by traditional color-color selections. These results highlight the power of self-supervised machine learning combined with SED fitting prioritization to uncover rare distant sources beyond conventional techniques. Our approach offers a scalable and robust framework for data mining and can be readily extended to forthcoming wide-field surveys such as Rubin/LSST, 4MOST, Euclid, and Roman, improving the census of high-redshift quasars and constraints on SMBH formation and evolution in the first billion years of the Universe.