An informed source separation of astrophysical ice analogs

The analysis of astrophysical ices and the determination of the compounds that are present in the molecular clouds play a fundamental role in order to predict the future evolution of the cloud, e.g., its transformation to protostellar bodies or the appearance of new radicals and molecules. Because of the difficulties of obtaining satellite data, the process is simulated first in the laboratory generating ice analogs under well controlled variables. In this case, the ice mixture is carried on allocating the different components in the appropriate concentrations and recording the spectrum of the aggregated ice. This process tries to simulate the real process of forming ice mantles under the environmental conditions of the interstellar medium. The spectrum of each ice can be modeled as the linear instantaneous superposition of the spectrum of the different compounds, so a source separation approach is proper. In addition, some priors about the sources and the mixing matrix entries can be assumed, obtaining an informed Bayesian approach to the problem. We present the results obtained with the variational Bayesian approach for simulated and real mixtures, showing the good performance of the algorithm.