Analysis of galactic spectra using active instance-based learning and domain knowledge

Abstract

In this paper we present an efficient solution, based on active and instance-based machine learning, to the problem of analyzing galactic spectra, an important problem in modern cosmology. The input to the algorithm is the energy flux received from the galaxy; its expected output is the set of stellar populations and dust abundances that make up the galaxy. Our experiments show very accurate results using both noiseless and noisy spectra, and also that a further improvement in accuracy can be obtained when we incorporate prior knowledge obtained from human experts. © Springer-Verlag Berlin Heidelberg 2004.