We discuss whether modern machine learning methods can be used to characterize the physical nature of the large number of objects sampled by the modern multi-band digital surveys .
In particular , we applied the MLPQNA ( Multi Layer Perceptron with Quasi Newton Algorithm ) method to the optical data of the Sloan Digital Sky Survey - Data Release 10 , investigating whether photometric data alone suffice to disentangle different classes of objects as they are defined in the SDSS spectroscopic classification .
We discuss three groups of classification problems : ( i ) the simultaneous classification of galaxies , quasars and stars ; ( ii ) the separation of stars from quasars ; ( iii ) the separation of galaxies with normal spectral energy distribution from those with peculiar spectra , such as starburst or starforming galaxies and AGN .
While confirming the difficulty of disentangling AGN from normal galaxies on a photometric basis only , MLPQNA proved to be quite effective in the three-class separation .
In disentangling quasars from stars and galaxies , our method achieved an overall efficiency of 91.31 \% and a QSO class purity of \sim 95 \% .
The resulting catalogue of candidate quasars/AGNs consists of \sim 3.6 million objects , of which about half a million are also flagged as robust candidates , and will be made available on CDS VizieR facility .