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The current role of data-driven science is constantly increasing itsimportance within Astrophysics, due to the huge amount of multi-wavelength datacollected every day, characterized by complex and high-volume informationrequiring efficient and as much as possible automated exploration tools.Furthermore, to accomplish main and legacy science objectives of future orincoming large and deep survey projects, such as JWST, LSST and Euclid, acrucial role is played by an accurate estimation of photometric redshifts,whose knowledge would permit the detection and analysis of extended andpeculiar sources by disentangling low-z from high-z sources and wouldcontribute to solve the modern cosmological discrepancies. The recentphotometric redshift data challenges, organized within several survey projects,like LSST and Euclid, pushed the exploitation of multi-wavelength andmulti-dimensional data observed or ad hoc simulated to improve and optimize thephotometric redshifts prediction and statistical characterization based on bothSED template fitting and machine learning methodologies. But they also provideda new impetus in the investigation on hybrid and deep learning techniques,aimed at conjugating the positive peculiarities of different methodologies,thus optimizing the estimation accuracy and maximizing the photometric rangecoverage, particularly important in the high-z regime, where the spectroscopicground truth is poorly available. In such a context we summarize what learnedand proposed in more than a decade of research.

Photometric redshifts with machine learning, lights and shadows on a complex data science use case