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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References
Allamandola, L.J., Bernstein, M.P., Sandford, S.A., Walker, R.L.: Evollution of Interestellar Ices. Space Science Reviews 90, 219–232 (1999)
Baratta, G.A., Palumbo, M.E., Strazzulla, G.: Laboratory and astronomical IR spectra: an experimental clue for their comparison. Astronomy and Astrophysics 357, 1045–1050 (1997)
Igual, J., Llinares, R., Salazar, A.: Source Separation of Astrophysical Ice Mixtures. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 368–375. Springer, Heidelberg (2006)
Igual, J., Llinares, R.: An informed source separation of astrophysical ice analogs. Digital Signal Processing 17, 947–964 (2007)
Igual, J., Llinares, R.: Nonnegative Matrix Factorization of Laboratory Astrophysical Ice Mixtures. IEEE Journal of Selected Topics in Signal Processing 2, 697–706 (2008)
Jiang, J., Liang, Y., Ozaki, Y.: Principles and methodologies in self-modeling curve resolution. Chemom. Intell. Lab. Syst. 71, 1–12 (2004)
de Juan, A., Cassasas, E., Tauler, R.: Sot-Modeling of analytical data. Encyclopedia of analytical chemistry: instrumentation and applications (2000)
Garrido, M., Rius, F.X., Larrechi, M.S.: Multivariate curve resolution–alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes. Analytical and Bioanalytical Chemistry 390, 2059–2066 (2008)
Cichocki, A., Zdunek, R.: Regularized alternating least squares algorithms for non-negative matrix/tensor factorization. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 793–802. Springer, Heidelberg (2007)
Cichocki, A., Zdunek, R.: NMFLAB for signal processing. Technical report, Laboratory for Advanced Brain Signal Processing, BSI RIKEN, Saitama, Japan (2006)
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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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