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dc.contributor.authorWenzl, L.
dc.contributor.authorSchindler, J.-T.
dc.contributor.authorFan, X.
dc.contributor.authorAndika, I.T.
dc.contributor.authorBañados, E.
dc.contributor.authorDecarli, R.
dc.contributor.authorJahnke, K.
dc.contributor.authorMazzucchelli, C.
dc.contributor.authorOnoue, M.
dc.contributor.authorVenemans, B.P.
dc.contributor.authorWalter, F.
dc.contributor.authorYang, J.
dc.date.accessioned2021-09-09T21:28:18Z
dc.date.available2021-09-09T21:28:18Z
dc.date.issued2021
dc.identifier.citationWenzl, L., Schindler, J.-T., Fan, X., Andika, I. T., Bañados, E., Decarli, R., Jahnke, K., Mazzucchelli, C., Onoue, M., Venemans, B. P., Walter, F., & Yang, J. (2021). Random Forests as a Viable Method to Select and Discover High-redshift Quasars. Astronomical Journal, 162(2).
dc.identifier.issn0004-6256
dc.identifier.doi10.3847/1538-3881/ac0254
dc.identifier.urihttp://hdl.handle.net/10150/661496
dc.description.abstractWe 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/{83}_{-9}^{+6} \% above redshift 5.6) while maintaining a high selection efficiency ({78}_{-8}^{+10} \%/{94}_{-8}^{+5} \%). 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 {148}_{-33}^{+41} new quasars below redshift 5.6 and {45}_{-8}^{+5} 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. © 2021. The American Astronomical Society. All rights reserved.
dc.language.isoen
dc.publisherAmerican Astronomical Society
dc.rightsCopyright © 2021. The American Astronomical Society. All rights reserved.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleRandom Forests as a Viable Method to Select and Discover High-redshift Quasars
dc.typeArticle
dc.typetext
dc.contributor.departmentSteward Observatory, University of Arizona
dc.identifier.journalAstronomical Journal
dc.description.noteImmediate access
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleAstronomical Journal
refterms.dateFOA2021-09-09T21:28:18Z


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