Spectroscopic classification of a complete sample of astrometrically-selected quasar candidates using Gaia DR2
K. E. Heintz, J. P. U. Fynbo, S. J. Geier, P. M{\o}ller, J.-K., Krogager, C. Konstantopoulou, A. de Burgos, L. Christensen, C. L. Steinhardt,, B. Milvang-Jensen, P. Jakobsson, E. H{\o}g, B. E. H. K. Arvedlund, C. R., Christiansen, T. B. Hansen, P. D. Henriksen, K. B. Kuszon

TL;DR
This study demonstrates that selecting quasars based solely on Gaia DR2 astrometry, specifically zero proper motion, yields a high-efficiency, unbiased sample that complements traditional optical and infrared methods, enhancing our understanding of quasar demographics.
Contribution
The paper introduces a purely astrometric method for quasar selection using Gaia DR2 data, achieving high efficiency and completeness, and compares it with existing optical and infrared techniques.
Findings
Astrometric selection yields ~60% efficiency at high Galactic latitudes.
Surface density of quasars is 20 deg$^{-2}$, peaking at z~1.5.
Astrometric selection is unbiased and can be improved with future Gaia data.
Abstract
Here we explore the efficiency and fidelity of a purely astrometric selection of quasars as point sources with zero proper motions in the {\it Gaia} data release 2 (DR2). We have built a complete candidate sample including 104 Gaia-DR2 point sources brighter than mag within one degree of the north Galactic pole (NGP), all with proper motions consistent with zero within 2 uncertainty. In addition to pre-existing spectra, we have secured long-slit spectroscopy of all the remaining candidates and find that all 104 stationary point sources in the field can be classified as either quasars (63) or stars (41). The selection efficiency of the zero-proper-motion criterion at high Galactic latitudes is thus . Based on this complete quasar sample we examine the basic properties of the underlying quasar population within the imposed limiting magnitude. We find that the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
