  We aim to identify QSO2 candidates in the redshift desert using optical and
infrared photometry. At this intermediate redshift range, most of the prominent
optical emission lines in QSO2 sources (e.g. CIV1549; [OIII]4959,5008) 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. 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. We validated the performance of AMELIA by
addressing the problem of identifying QSO2s at 1 $\leq$ z $\leq$ 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]3426 emitters at z $\sim$
1.1, which are highly likely to contain obscured AGNs. We used X-ray and radio
cross-matching 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.
