METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts

Understand

A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's).

• A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space.
• Machine learning based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set.
• These estimates, however, are not easy to characterize in terms of a photo-z Probability Density Function (PDF), due to the fact that the analytical relation mapping the photometric parameters onto the redshift space is virtually unknown.