  With the launch of eROSITA (extended Roentgen Survey with an Imaging
Telescope Array), successfully occurred on 2019 July 13, we are facing the
challenge of computing reliable photometric redshifts for 3 million of active
galactic nuclei (AGNs) over the entire sky, having available only patchy and
inhomogeneous ancillary data. While we have a good understanding of the photo-z
quality obtainable for AGN using spectral energy distribution (SED)-fitting
technique, we tested the capability of machine learning (ML), usually reliable
in computing photo-z for QSO in wide and shallow areas with rich spectroscopic
samples. Using MLPQNA as example of ML, we computed photo-z for the
X-ray-selected sources in Stripe 82X, using the publicly available photometric
and spectroscopic catalogues. Stripe 82X is at least as deep as eROSITA will be
and wide enough to include also rare and bright AGNs. In addition, the
availability of ancillary data mimics what can be available in the whole sky.
We found that when optical, and near- and mid-infrared data are available, ML
and SED fitting perform comparably well in terms of overall accuracy, realistic
redshift probability density functions, and fraction of outliers, although they
are not the same for the two methods. The results could further improve if the
photometry available is accurate and including morphological information.
Assuming that we can gather sufficient spectroscopy to build a representative
training sample, with the current photometry coverage we can obtain reliable
photo-z for a large fraction of sources in the Southern hemisphere well before
the spectroscopic follow-up, thus timely enabling the eROSITA science return.
The photo-z catalogue is released here.
