• search hit 1 of 1
Back to Result List

A Deep Learning System to Transform Cross-Section Spectra to Varying Environmental Conditions

  • 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Elisabeth Moser, Simon JobstORCiD, Rudolf Bierl, Frank Jenko
DOI:https://doi.org/10.1016/j.vibspec.2022.103410
ISSN:0924-2031
Parent Title (English):Vibrational Spectroscopy
Publisher:Elsevier
Document Type:Article
Language:English
Year of first Publication:2022
Release Date:2022/07/24
Tag:Deep learning; Pressure broadening; Pseudo-line lists; Simulation; Spectroscopy; Transfer learning
Volume:122
Issue:September
Article Number:103410
Institutes:Fakultät Angewandte Natur- und Kulturwissenschaften
Fakultät Angewandte Natur- und Kulturwissenschaften / Sensorik-Applikationszentrum (SappZ)
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG