Random forests as a viable method to select and discover high-redshift quasars

• Verfasst von: Wenzl, Lukas [VerfasserIn]
• Verfasst von: Schindler, Jan-Torge [VerfasserIn]
• Verfasst von: Fan, Xiaohui [VerfasserIn]
• Verfasst von: Andika, Irham Taufik [VerfasserIn]
• Verfasst von: Bañados Torres, Eduardo [VerfasserIn]
• Verfasst von: Decarli, Roberto [VerfasserIn]
• Verfasst von: Jahnke, Knud [VerfasserIn]
• Verfasst von: Mazzucchelli, Chiara [VerfasserIn]
• Verfasst von: Onoue, Masafusa [VerfasserIn]
• Verfasst von: Venemans, Bram P. [VerfasserIn]
• Verfasst von: Walter, Fabian [VerfasserIn]
• Verfasst von: Yang, Jinyi [VerfasserIn]
• Titel: Random forests as a viable method to select and discover high-redshift quasars
• Verf.angabe: Lukas Wenzl, Jan-Torge Schindler, Xiaohui Fan, Irham Taufik Andika, Eduardo Bañados, Roberto Decarli, Knud Jahnke, Chiara Mazzucchelli, Masafusa Onoue, Bram P. Venemans, Fabian Walter, and Jinyi Yang
• E-Jahr: 2021
• Jahr: 2021 July 27
• Umfang: 18 S.
• Teil: volume:162
• Teil: year:2021
• Teil: number:2
• Teil: elocationid:72
• Teil: pages:1-18
• Teil: extent:18
• Fussnoten: Gesehen am 20.10.2021
• Titel Quelle: Enthalten in: The astronomical journal
• Ort Quelle: London : Institute of Physics Publ., 1998
• Jahr Quelle: 2021
• Band/Heft Quelle: 162(2021), 2, Artikel-ID 72, Seite 1-18
• ISSN Quelle: 1538-3881
• DOI: doi:10.3847/1538-3881/ac0254
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1774607115

Abstract

We present a method of selecting quasars up to redshift ≈6 with random forests, a supervised machine-learning method, applied to Pan-STARRS1 and WISE data. We find that, thanks to the increasing set of known quasars, we can assemble a training set that enables supervised machine-learning algorithms to become a competitive alternative to other methods up to this redshift. We present a candidate set for the redshift range 4.8-6.3, which includes the region around z = 5.5 where selecting quasars is difficult due to their photometric similarity to red and brown dwarfs. We demonstrate that, under our survey restrictions, we can reach a high completeness (66% ± 7% below redshift 5.6/ above redshift 5.6) while maintaining a high selection efficiency (/). Our selection efficiency is estimated via a novel method based on the different distributions of quasars and contaminants on the sky. The final catalog of 515 candidates includes 225 known quasars. We predict the candidate catalog to contain additional new quasars below redshift 5.6 and above, and we make the catalog publicly available. Spectroscopic follow-up observations of 37 candidates led us to discover 20 new high redshift quasars (18 at 4.6 ≤ z ≤ 5.5, 2 z ∼ 5.7). These observations are consistent with our predictions on efficiency. We argue that random forests can lead to higher completeness because our candidate set contains a number of objects that would be rejected by common color cuts, including one of the newly discovered redshift 5.7 quasars.