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We present an exploration of the expected detection of the earliest active galactic nuclei (AGNs) in the Universe from state-of-art galaxy formation and evolution semi-analytic models and hydrodynamical simulations. We estimate the number and radiative characteristics of supermassive black holes (SMBHs) at z  ≥ 6, a redshift range that will be intensively explored by the next generation of telescopes, in particular in the radio through the Square Kilometre Array (SKA) and at high energies with ESA's Athena X-ray Observatory. We find that Athena will be able to observe over 5000 AGN deg−2 at the Epoch of Re-ionization (EoR), 6 ≤  z  ≤ 10. Similarly, for the same redshift range the models/simulations suggest that SKA will detect at least 400 AGN deg−2. Additionally, we stress the importance of the volume of the simulation box as well as the initial physical conditions of the models/simulations on their effect on the luminosity functions (LFs) and the creation of the most massive SMBHs that we currently observe at the EoR. Furthermore, following the evolution of the accretion mode of the SMBHs in each model/simulation, we show that, while the quasar dominates over the radio mode at the EoR, detection at radio wavelengths still reaches significant numbers even at the highest redshifts. Finally, we present the effect that the radiative efficiency has on the LFs by comparing results produced with a constant value for the radiative efficiency and more complex calculations based on the spin of each SMBH. [ABSTRACT FROM AUTHOR]

We present ELDAR, a new method that exploits the potential of medium- and narrow-band filter surveys to securely identify active galactic nuclei (AGN) and determine their redshifts. Our methodology improves on traditional approaches by looking for AGN emission lines expected to be identified against the continuum, thanks to the width of the filters. To assess its performance, we apply ELDAR to the data of the ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey, which covered an effective area of 2.38 deg2 with 20 contiguous medium-band optical filters down to F814W ≃ 24.5. Using two different configurations of ELDAR in which we require the detection of at least two and three emission lines, respectively, we extract two catalogues of type-I AGN. The first is composed of 585 sources (79 per cent of them spectroscopically unknown) down to F814W = 22.5 at zphot > 1, which corresponds to a surface density of 209 deg-2. In the second, the 494 selected sources (83 per cent of them spectroscopically unknown) reach F814W= 23 at zphot > 1.5, for a corresponding number density of 176 deg-2. Then, using samples of spectroscopically known AGN in the ALHAMBRA fields, for the two catalogues we estimate a completeness of 73 per cent and 67 per cent, and a redshift precision of 1.01 per cent and 0.86 per cent (with outliers fractions of 8.1 per cent and 5.8 per cent). At z > 2, where our selection performs best, we reach 85 per cent and 77 per cent completeness and we find no contamination from galaxies. [ABSTRACT FROM AUTHOR]

Active Galactic Nuclei (AGN) are relevant sources of radiation that might have helped reionising the Universe during its early epochs. The super-massive black holes (SMBHs) they host helped accreting material and emitting large amounts of energy into the medium. Recent studies have shown that, for epochs earlier than z ∼ 5 , the number density of SMBHs is on the order of few hundreds per square degree. Latest observations place this value below 300 SMBHs at z ≳ 6 for the full sky. To overcome this gap, it is necessary to detect large numbers of sources at the earliest epochs. Given the large areas needed to detect such quantities, using traditional redshift determination techniques—spectroscopic and photometric redshift—is no longer an efficient task. Machine Learning (ML) might help obtaining precise redshift for large samples in a fraction of the time used by other methods. We have developed and implemented an ML model which can predict redshift values for WISE-detected AGN in the HETDEX Spring Field. We obtained a median prediction error of σ z N = 1.48 × (z Predicted − z True) / (1 + z True) = 0.1162 and an outlier fraction of η = 11.58 % at (z Predicted − z True) / (1 + z True) > 0.15 , in line with previous applications of ML to AGN. We also applied the model to data from the Stripe 82 area obtaining a prediction error of σ z N = 0.2501 . [ABSTRACT FROM AUTHOR]