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

Project title: Developing an artificial neural network model to analyse emission spectra of high-frequency electrodeless lamps
Project contract number: 1.1.1.9/LZP/1/24/023
Research manager (Postdoctoral fellow): Dr. Natalja Zorina
Project partners: none
Project implementation time: 01.03.2025 - 29.02.2028
Total project funding: 184 140 EUR (incl. ERDF 156 519 EUR)
Aim of the project: to create an artificial neural network (ANN) model capable of analysing the emission spectra of high-frequency electrodeless lamps (HFEDL). Specifically, the model will be trained to recognise patterns and dependencies in the data associated with the production and operating conditions of the lamps, using the spectral data obtained in a specific range of frequencies
Project results: 2 scientific articles in SCI journals, 1 marketable product/technology

• PIERS 2025 (Progress In Electromagnetics Research Symposium) 5-9 November 2025 Chiba, Japan. Report ‘Mercury Light Source Optimization for Zeeman Atomic Absorption Spectroscopy’ (In Conference Proceedings)
• 36^th International Conference on Phenomena in Ionized Gases (ICPIG)  20-25 July, 2025, Aix en Provence, France. Report ‘Automated Processing of High-Frequency Electrodeless-Lamp Spectra’ (in Book of Abstracts)
• 6^th European Conference on Plasma Diagnostics (ECPD) 7-10 April, 2025, Prague, Czechia. Report ‘Training of Artificial Neural Network for HFEDL Spectral Diagnostics’ (in Book of Abstracts)
• LU FST ASI research workshop (03.04.2025.)

A. Abola, G. Revalde, A. Skudra, N. Zorina and R. Veilande, "Mercury Light Source Optimization for Zeeman Atomic Absorption Spectroscopy," 2025 Photonics & Electromagnetics Research Symposium - Fall (PIERS-Fall), Chiba, Japan, 2025, pp. 1-6, doi: 10.23919/PIERS-Fall62445.2025.11393975