Probing the near universe with neural networks
We outline the problems encountered in classifying large
volumes of photometric data produced by a modern digital
survey such as the SDSS (Sloan Digital Sky Survey), pointing
out that most of these problems are produced by the
degeneracy introduced in the parameter space by the lack of
reliable information on the objects distance. In order to
partially remove such degeneracy we implemented a supervised
neural network approach to the determination of photometric
The method, even though of general validity, was fine tuned
to match the characteristics of the Sloan Digital Sky Survey
and takes into account the uneven distribution of measured
redshifts in the SDSS spectroscopic subsample.
It consists of a two step approach. In the first step,
objects are classified in nearby (z<0.25) and distant
(0.25<z<0.50), and in the second step two different networks
are separately trained on objects belonging to the two
redshift ranges. The final results lead to a robust sigma
0.206 over the redshift range [ 0.01, 0.48].
The network was then applied to som 32 million objects from
the SDSS to produce a 3-D map of the nearby universe.
The final redshifts were used as input parameter to an
unsupervised neural classifier and the resulting clusters