Stefano Cavuoti, Massimo Brescia, Giuseppe Longo, Amata Mercurio
Context. Since the advent of modern multiband digital sky surveys, photometric redshifts (photo-z's) have become relevant if not crucial to many fields of observational cosmology, from the characterization of cosmic structures, to weak and strong lensing. Aims. We describe an application to an astrophysical context, namely the evaluation of photometric redshifts, of MLPQNA, a machine learning method based on Quasi Newton Algorithm. Methods. Empirical methods for photo-z's evaluation are based on the interpolation of a priori knowledge (spectroscopic redshifts or SED templates) and represent an ideal test ground for neural networks based methods. The MultiLayer Perceptron with Quasi Newton learning rule (MLPQNA) described here is a computing effective implementation of Neural Networks and is offered to the community through the DAMEWARE (DAta Mining & Exploration Web Application REsource) infrastructure. Results. The PHAT contest (Hildebrandt et al. 2010) provides a standard dataset to test old and new methods for photometric redshift evaluation and with a set of statistical indicators which allow a straightforward comparison among different methods. When applied to the PHAT1 dataset, MLPQNA obtains very competitive accuracies in terms of bias, RMS (Root Mean Square) and outlier percentage, scoring as the second most effective empirical method among those which have so far participated to the contest.
View original:
http://arxiv.org/abs/1206.0876
No comments:
Post a Comment