TY - JOUR A1 - Moser, Elisabeth A1 - Jobst, Simon A1 - Bierl, Rudolf A1 - Jenko, Frank T1 - A Deep Learning System to Transform Cross-Section Spectra to Varying Environmental Conditions JF - Vibrational Spectroscopy N2 - Absorption cross-sections provide a basis for many gas sensing applications. Therefore, any error in molecular cross-sections caused by varying environmental conditions propagates to spectroscopic applications. Original molecular cross-sections in varying environmental conditions can only be simulated for some molecules, whereas for most multi-atom molecules, one must rely on high-precision measurements at certain environmental configurations. In this study, a deep learning system trained with simulated absorption cross-sections for predicting cross-sections at a different pressure configuration is presented. The system’s capability to transfer to measured, multi-atom cross-sections is demonstrated. Thus, it provides an alternative to (pseudo-) line lists whenever the required information for simulation is unavailable. The predictive performance of the system was evaluated on validation data via simulation, and its transfer learning capabilities were demonstrated on actual measurement chlorine nitrate data. From the comparison between the system and line lists, the system shows slightly worse performance than pseudo-line lists but its predictive quality is still deemed acceptable with less than 5% relative integral change with a highly localized error around the peak center. This opens a promising way for further research to use deep learning to simulate the effect of varying environmental conditions on absorption cross-sections. KW - Spectroscopy KW - Deep learning KW - Simulation KW - Pressure broadening KW - Pseudo-line lists KW - Transfer learning Y1 - 2022 U6 - https://doi.org/10.1016/j.vibspec.2022.103410 SN - 0924-2031 VL - 122 IS - September PB - Elsevier ER -