Details

Autor(en) / Beteiligte

• Pérez-Ràfols, Ignasi
• Abramo, Luis Raul
• Martínez-Solaeche, Ginés
• Pieri, Matthew M.
• Queiroz, Carolina
• Rodrigues, Natália V. N.
• Bonoli, Silvia
• Chaves-Montero, Jonás
• Morrison, Sean S.
• Alcaniz, Jailson
• Benitez, Narciso
• Carneiro, Saulo
• Cenarro, Javier
• Cristóbal-Hornillos, David
• Dupke, Renato
• Ederoclite, Alessandro
• González Delgado, Rosa M.
• Hernán-Caballero, Antonio
• López-Sanjuan, Carlos
• Marín-Franch, Antonio
• Marra, Valerio
• Mendes de Oliveira, Claudia
• Moles, Mariano
• Sodré Jr, Laerte
• Taylor, Keith
• Varela, Jesús
• Vázquez Ramió, Héctor

Titel

The miniJPAS survey quasar selection: IV. Classification and redshift estimation with SQUEzE

Ist Teil von

• Astronomy and astrophysics (Berlin), 2023-10, Vol.678, p.A144

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Freely accessible e-journals

Beschreibungen/Notizen

• Aims . Quasar catalogues from photometric data are used in a variety of applications including those targeting spectroscopic follow-up, measurements of supermassive black hole masses, Baryon Acoustic Oscillations, or non-Gaussianities. Here, we present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. miniJPAS is a small proof-of-concept survey covering 1 deg 2 with the full J-PAS filter system, consisting of 54 narrow filters and 2 broader filters covering the entire optical wavelength range. Methods . This work is based on the machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a ‘black box’ when compared with other classifiers. Another key advantage is that the use of user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, we have adapted it for multi-band photometric data; that is we treat multiple narrow-band filters as very low-resolution spectra. We trained our models using specialised mocks. We estimated our redshift precision using the normalised median absolute deviation, σ NMAD , applied to our test sample. Results . Our test sample returns an f 1 score (effectively the purity and completeness) of 0.49 for high- z quasars (with z ≥ 2.1) down a to magnitude of r = 24.3 and 0.24 for low- z quasars (with z < 2.1), also down to a magnitude of r = 24.3. For high- z quasars, this goes up to 0.9 for magnitudes of r < 21.0. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a σ NMAD = 0.92% for the entire sample, reduced to 0.81% for r < 22.5 and 0.74% for r < 21.3. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.