Searching for high-redshift quasars with the Photometric Vision Quasar Network (PVQNet)

Abstract The discovery of high-redshift quasars (HQSOs) hinges on identifying candidates through photometric data from extensive sky surveys, followed by confirmation through spectroscopic analysis. This paper introduces the Photometric Vision Quasar Network (PVQNet), designed to effectively extract both local and global features from SDSS photometric images in five bands to select HQSOs with high-redshifts ($z>5$). Our model, trained on 2322 negative ($z \lt 5$) and 2322 positive ($z>5$) quasar images from SDSS DR17, achieves an accuracy of $94.03\%$ on the test dataset, surpassing the mainstream random forest method using the photometric catalog features by $6\%$. PVQNet is further validated on 307 newly discovered quasars from DESI ($z>5$) and Pan-STARRS ($z>5.6$) surveys, achieving accuracy rates of $91.11\%$ and $94.2\%$ at $|z-5|\ge 0.4$ and $|z-5|\ge 0.5$, respectively. When applied to 83256 candidates from the DR17 specObjAll catalog with low-quality spectra, PVQNet successfully identifies 682 new HQSO candidates with a confidence greater than 0.92. Utilizing a $g-r/r-i$ color-cut with DR16Q, we filter out $97\%$ of low-redshift quasars ($z \lt 2.75$) while maintaining $46\%$ completeness in HQSO identification, resulting in 107 final candidates. In summary, we explore the contribution of image features across different bands to select HQSOs, combining PVQNet with a color-cut approach to efficiently identify candidates, even with limited multiband photometric data and potentially unreliable spectroscopic pipeline results due to low-quality spectra.