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Owl-z: a Bayesian tool to select z \geq 7 quasars

Meriam Ezziati, Roser Pello, Jean-Gabriel Cuby, Pierre Pudlo, François-Xavier Dupé, Jean-Charles Lambert, Jean-Charles Cuillandre, Olivier Ilbert, Sylvain de la Torre, Stéphane Arnouts, Eric Jullo, Daming Yang

TL;DR

Owl-z presents a Bayesian classifier for identifying z geq 7 quasars in wide field optical and NIR surveys, modeling three populations: high z quasars, MLT dwarfs, and intermediate redshift contaminant galaxies. The method leverages SED templates, luminosity functions, and survey priors to compute posterior probabilities and MAP parameters, with outputs including photometric redshifts and best-fit SEDs. Validation combines real high z quasar re identifications and simulated Euclid Wide Survey catalogs, reporting completeness, purity, and a global F measure that favors bright quasars and can be tuned via the probability threshold ζ. The tool demonstrates strong re identification performance, robust photometric redshifts, and competitive completeness while outlining strategies and limitations for applying to Euclid and future surveys. Owl-z thus offers a scalable, adaptable framework for prioritizing spectroscopic follow-up and refining high redshift quasar demographics in upcoming large photometric datasets.

Abstract

This paper presents Owl-z, a Bayesian code aiming at identifying z \geq 7 quasars in wide field optical and near-infrared surveys. By construction,the code can also be used to select objects that contaminate the high-z quasar population, i.e. brown dwarfs and early-type galaxies at intermediate redshifts. The code can be adapted for the selection of high-z galaxies, and although it has been tuned to the Euclid Wide Survey, it can be easily adapted to other photometric surveys. The code input data are the object's photometric data and its galactic longitude and latitude, and the code output data are the probabilities of the modelled populations of high-z quasars, brown dwarfs and early-type galaxies at intermediate redshift. As part of the validation, Owl-z could re-identify all spectroscopically confirmed quasars at z \geq 7, demonstrating the code's versatility in applying to different photometric catalogues. The performance of Owl-z, based on a metric combining completeness and purity called F-measure, is analysed in the case of Euclid using simulated data in a wide range of redshifts (7 \leq z \leq 12) and H-band Euclid magnitudes (18 \leq H_E \leq 24.5). The results show that Owl-z reaches full performance for bright sources (H_E \lesssim 22), independently of the redshift. We show that the probability threshold used to select promising quasar candidates can be adjusted after processing to fine-tune the F-measure value of candidates depending on their magnitude and redshift estimates. We show that for objects brighter than about two magnitudes above the survey detection limit, Owl-z provides a classification that will facilitate the optimisation of photometric and spectroscopic confirmation campaigns. In conclusion, Owl-z is a powerful public tool to help select high-z quasars, brown dwarfs or early-type galaxies at intermediate redshifts in Euclid or other wide-field surveys.

Owl-z: a Bayesian tool to select z \geq 7 quasars

TL;DR

Owl-z presents a Bayesian classifier for identifying z geq 7 quasars in wide field optical and NIR surveys, modeling three populations: high z quasars, MLT dwarfs, and intermediate redshift contaminant galaxies. The method leverages SED templates, luminosity functions, and survey priors to compute posterior probabilities and MAP parameters, with outputs including photometric redshifts and best-fit SEDs. Validation combines real high z quasar re identifications and simulated Euclid Wide Survey catalogs, reporting completeness, purity, and a global F measure that favors bright quasars and can be tuned via the probability threshold ζ. The tool demonstrates strong re identification performance, robust photometric redshifts, and competitive completeness while outlining strategies and limitations for applying to Euclid and future surveys. Owl-z thus offers a scalable, adaptable framework for prioritizing spectroscopic follow-up and refining high redshift quasar demographics in upcoming large photometric datasets.

Abstract

This paper presents Owl-z, a Bayesian code aiming at identifying z \geq 7 quasars in wide field optical and near-infrared surveys. By construction,the code can also be used to select objects that contaminate the high-z quasar population, i.e. brown dwarfs and early-type galaxies at intermediate redshifts. The code can be adapted for the selection of high-z galaxies, and although it has been tuned to the Euclid Wide Survey, it can be easily adapted to other photometric surveys. The code input data are the object's photometric data and its galactic longitude and latitude, and the code output data are the probabilities of the modelled populations of high-z quasars, brown dwarfs and early-type galaxies at intermediate redshift. As part of the validation, Owl-z could re-identify all spectroscopically confirmed quasars at z \geq 7, demonstrating the code's versatility in applying to different photometric catalogues. The performance of Owl-z, based on a metric combining completeness and purity called F-measure, is analysed in the case of Euclid using simulated data in a wide range of redshifts (7 \leq z \leq 12) and H-band Euclid magnitudes (18 \leq H_E \leq 24.5). The results show that Owl-z reaches full performance for bright sources (H_E \lesssim 22), independently of the redshift. We show that the probability threshold used to select promising quasar candidates can be adjusted after processing to fine-tune the F-measure value of candidates depending on their magnitude and redshift estimates. We show that for objects brighter than about two magnitudes above the survey detection limit, Owl-z provides a classification that will facilitate the optimisation of photometric and spectroscopic confirmation campaigns. In conclusion, Owl-z is a powerful public tool to help select high-z quasars, brown dwarfs or early-type galaxies at intermediate redshifts in Euclid or other wide-field surveys.
Paper Structure (26 sections, 20 equations, 20 figures, 7 tables)

This paper contains 26 sections, 20 equations, 20 figures, 7 tables.

Figures (20)

  • Figure 1: NIR (top) and Optical-NIR (bottom) colours for the three classes of objects considered in this work, using Euclid filters. Black solid lines display quasars in the redshift domains captured by the filters, with redshifts indicated directly on the lines. Blue lines display galaxies at $(1\leq z \leq 2)$, susceptible to contaminating the quasar samples. MLT: stars M, triangles L, and diamonds T. For the M, L and T, filled: $z-\YE$ and hollow: $\IE-\YE$
  • Figure 2: The expected number of quasars per redshift bin in the 15,000 deg$^2$ EWS given the high-$z$ quasar Luminosity Function adopted in this paper. In blue are shown the numbers of quasars expected up to $\HE<24$, in crimson with $\HE<22.5$ and in green with $\HE<21.5$
  • Figure 3: WISE $W1-W2$ colours of MLT dwarfs from Best2017 shown in blue, green, and red, respectively, with the values indicating their spectral type.
  • Figure 4: Brown dwarf number counts as a function of H-band magnitude for the thin and thick discs in two different field locations ($b=90^{\circ}$ and ($l$,$b$)=(90$^{\circ}$,30$^{\circ}$)). Top: M6 to M9 spectral types, middle: L0 to L9 and bottom: T0 to T8.
  • Figure 5: Ratio of the surface density of brown dwarfs of spectral type M6 to T8 to the surface density of $z>7$ quasars over the Euclid footprint shown in Galactic coordinates and galactic projection. Top: for magnitude = 20, bottom for magnitude = 24.
  • ...and 15 more figures