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Abstract:

The statistical distribution and evolution of key properties of active galactic nuclei (AGN), such as their accretion rate, mass, and spin, remains a subject of open debate in astrophysics. The ESA Euclid space mission, launched on July 1 2023, promises a breakthrough in this field. We create detailed mock catalogues of AGN spectra from the rest-frame near-infrared down to the ultraviolet – including emission lines – to simulate what Euclid will observe for both obscured (type 2) and unobscured (type 1) AGN. We concentrate on the red grisms of the NISP instrument, which will be used for the wide-field survey, opening a new window for spectroscopic AGN studies in the near-infrared. We quantify the efficiency in the redshift determination as well as in retrieving the emission line flux of the Hα+[N II] complex, as Euclid is mainly focused on this emission line, given that it is expected to be the brightest one in the probed redshift range. Spectroscopic redshifts are measured for 83% of the simulated AGN in the interval where the Hα is visible (i.e. 0.89 <  z <  1.83 at a line flux of > 2 × 10−16 erg s−1 cm−2, encompassing the peak of AGN activity at z ≃ 1 − 1.5) within the spectral coverage of the red grism. Outside this redshift range, the measurement efficiency decreases significantly. Overall, a spectroscopic redshift iscorrectly determined for about 90% of type 2 AGN down to an emission line flux of roughly 3 × 10−16 erg s−1 cm−2, and for type 1 AGN down to 8.5 × 10−16 erg s−1 cm−2. Recovered values for black hole mass show a small offset with respect to the input values by about 10%, but the agreement is good overall. With such a high spectroscopic coverage at z <  2, we will be able to measure AGN demography, scaling relations, and clustering from the epoch of the peak of AGN activity down to the present-day Universe for hundreds of thousands of AGN with homogeneous spectroscopic information

Abstract:

Current and future imaging surveys require photometric redshifts (photo- z s) to be estimated for millions of galaxies. Improving the photo- z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo- z estimates by simultaneously predicting the broadband photo- z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo- z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo- z s that are 13% more precise down to magnitude i AB < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo- z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z > 1. Applying this technique to deeper samples is crucial for future surveys such as Euclid or LSST. For simulated data, training on a sample with i AB < 23, the method reduces the photo- z scatter by 16% for all galaxies with i AB < 25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo- z s, which reduces the photo- z scatter by 20% in the COSMOS field.