Developing an artificial neural network model to analyze emission spectra of high-frequency electrodeless lamps

The Data Management Plan (DMP) is developed in the framework of the project 1.1.1.9 Research application No 1.1.1.9/LZP/1/24/023 of the Activity "Post-doctoral Research" " Developing an artificial neural network model to analyze emission spectra of high-frequency electrodeless lamps ". Project manager: Ph. D. Natalja Zorina. Project implementation time: 01.03.2025 - 29.02.2028. Total project funding: 184 140 EUR (incl. ERDF 156 519 EUR).
Summary of the postdoctoral research grant project:
High-frequency electrodeless discharge lamps (HFEDLs) combine compact design, long lifetime, and stable narrowband emission (FWHM 0.03–0.12 cm⁻¹), making them a competitive alternative to hollow cathode lamps in atomic absorption spectrometry. Operating with heavy metal vapors (e.g., mercury, thallium) and rare gases (e.g., argon, xenon) as buffer gases, HFEDLs generate low-pressure discharges sustained by high-frequency fields. Their high intensity and reduced contamination improve detection limits, but the resulting spectra often contain multiple closely spaced and overlapping lines from different elements. This complicates manual interpretation and underscores the need for accurate and efficient spectral analysis to optimize lamp performance and ensure reproducible results in applications such as atomic absorption spectroscopy, plasma diagnostics, and environmental monitoring.
The research aims to develop an artificial neural network (ANN) model for analyzing the emission spectra of high-frequency electrodeless lamps (HFEDL). The model will be trained to identify characteristic patterns in spectral data and explore their connection to relevant lamp parameters and operating conditions within a defined frequency range.