Abstract
The determination of the compounds that are present in molecular clouds is carried out from the study of the infrared spectrum of astrophysical ices. This analysis plays a fundamental role in the prediction of the future evolution of the cloud under study. The process is simulated in the laboratory under similar conditions of thermal and energetic processing, recording the infrared absorption spectrum of the resultant ice. 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. We propose the use of Alternating Least Squares (ALS) and a Regularized version (RALS) to identify the molecules that are present in the ice mixtures. Since the spectra and abundances are non-negative, a non-negativity constraint can be applied to obtain solutions with physical meaning. We perform several simulations of synthetic mixtures of ices in order to compare both solutions and to show the usefulness of the approach.
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Llinares, R., Igual, J., Miro-Borras, J., Camacho, A. (2010). Analysis of Astrophysical Ice Analogs Using Regularized Alternating Least Squares. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_26
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DOI: https://doi.org/10.1007/978-3-642-15819-3_26
Publisher Name: Springer, Berlin, Heidelberg
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