Identifying type II quasars at intermediate redshift with few-shot learning photometric classification

¹ Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal
e-mail: pedro.cunha@astro.up.pt
² Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal
³ DTx – Digital Transformation CoLab, Building 1, Azurém Campus, University of Minho, 4800-058 Guimarães, Portugal
⁴ Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, Edifício C8, Campo Grande, 1749-016 Lisboa, Portugal
⁵ Instituto de Astrofísica e Ciências do Espaço, Universidade de Lisboa, OAL, Tapada da Ajuda, 1349-018 Lisboa, Portugal

Received: 15 March 2023
Accepted: 14 May 2024

Abstract

Context. A sub-population of AGNs where the central engine is obscured are known as type II quasars (QSO2s). These luminous AGNs have a thick and dusty torus that obscures the accretion disc from our line of sight. Thus, their special orientation allows for detailed studies of the AGN-host co-evolution. Increasing the sample size of QSO2 sources in critical redshift ranges is crucial for understanding the interplay of AGN feedback, the AGN-host relationship, and the evolution of active galaxies.

Aims. We aim to identify QSO2 candidates in the ‘redshift desert’ using optical and infrared photometry. At this intermediate redshift range (i.e. 1 ≤ ɀ ≤ 2), most of the prominent optical emission lines in QSO2 sources (e.g. CIVλl549; [OIII]λλ4959, 5008) fall either outside the wavelength range of the SDSS optical spectra or in particularly noisy wavelength ranges, making QSO2 identification challenging. Therefore, we adopted a semi-supervised machine learning approach to select candidates in the SDSS galaxy sample.

Methods. Recent applications of machine learning in astronomy focus on problems involving large data sets, with small data sets often being overlooked. We developed a ‘few-shot’ learning approach for the identification and classification of rare-object classes using limited training data (200 sources). The new AMELIA pipeline uses a transfer-learning based approach with decision trees, distance-based, and deep learning methods to build a classifier capable of identifying rare objects on the basis of an observational training data set.

Results. We validated the performance of AMELIA by addressing the problem of identifying QSO2s at 1 ≤ ɀ ≤ 2 using SDSS and WISE photometry, obtaining an F1-score above 0.8 in a supervised approach. We then used AMELIA to select new QSO2 candidates in the ‘redshift desert’ and examined the nature of the candidates using SDSS spectra, when available. In particular, we identified a sub-population of [NeV]λ3426 emitters at ɀ ~ 1.1, which are highly likely to contain obscured AGNs. We used X-ray and radio crossmatching to validate our classification and investigated the performance of photometric criteria from the literature showing that our candidates have an inherent dusty nature. Finally, we derived physical properties for our QSO2 sample using photoionisation models and verified the AGN classification using an SED fitting.

Conclusions. Our results demonstrate the potential of few-shot learning applied to small data sets of rare objects, in particular QSO2s, and confirms that optical-IR information can be further explored to search for obscured AGNs. We present a new sample of candidates to be further studied and validated using multi-wavelength observations.