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| Paper: |
Prediction of molecular parameters from astronomical emission lines, using Neural Networks |
| Volume: |
535, Astronomical Data Analysis Software and Systems XXXI |
| Page: |
107 |
| Authors: |
Barrientos, A.; Solar, M. |
| Abstract: |
We investigate if neural networks can be used to predict excitation temperature (Tex) and column density (log(N)) for a given spectrum. We used as test cases the spectra of CO, HCO+, SiO and CH3CN between 80 and 400 GHz. Training spectra were generated with MADCUBA, a state-of-the-art spectral analysis tool. Our algorithm was designed to allow the generation of predictions for multiple molecules in parallel, in a way that is scalable, and presents a linear speedup. We were able to predict the column density and excitation temperature of these molecules with a mean absolute error of 8.5% for CO, 4.1% for HCO+, 1.5% for SiO and 1.6% for CH3CN. |
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