Nonnegative Matrix Factorization of Laboratory Astrophysical Ice Mixtures

We present an application of nonnegative matrix factorization (NMF) in astrophysics. It consists of the study of ice mixtures obtained in the laboratory. They simulate real astrophysical ices. The goal is to identify the molecules that are present in the ice mixtures. The data correspond to the infrared absorption spectra of ices formed by different combinations of molecules. The spectra and abundances are nonnegative, allowing the application of NMF. In addition, the statistics of the spectra correspond to supergaussian signals, so a sparseness restriction can be added. We review some NMF algorithms imposing sparseness in a Bayesian framework. We perform several simulations with artificial mixtures of ices in order to compare the algorithms and with real mixtures to show the usefulness of the approach. NMF is revealed as a powerful technique to analyze large databases in order to determine the compounds present in every ice.