Thursday 23 January 2025
A team of researchers has developed a novel approach for detecting spectral lines in atomic emission spectra using neural networks. The method, which combines Fourier transform (FT) spectroscopy and deep learning, shows significant promise for automating the process of identifying spectral lines in complex atomic spectra.
The team trained a bidirectional long short-term memory (LSTM) – feedforward convolutional neural network (FCNN) model on simulated spectra of nickel (Ni) and neodymium (Nd), two elements with complex atomic structures. The model was able to detect over 95% of the spectral lines present in human-made line lists, outperforming traditional peak-finding methods.
The researchers’ approach is particularly effective for detecting weak lines and blends, which are often challenging to identify manually. The neural network model learns to recognize patterns in the spectrum that indicate the presence of a line, even if it’s not immediately apparent.
One of the key advantages of this method is its ability to handle instrumental resolution-limited ringing, a common issue in FT spectroscopy. The model is able to learn the characteristics of the instrumental profile and effectively remove noise and artifacts from the spectrum.
The researchers tested their approach on nine Ni-He HCL FT spectra covering 1800-70,000 cm^-1 and a single Nd-Ar PDL FT spectrum with a line density of over 9,400 lines per square centimeter. The results were impressive, with the neural network model detecting over 13,400 lines in total.
The team’s approach has significant implications for the field of atomic spectroscopy. By automating the process of identifying spectral lines, researchers can focus on more complex and challenging problems, such as analyzing spectra from high-temperature plasmas or understanding the behavior of atoms at extremely low temperatures.
In addition to its potential applications in atomic spectroscopy, this approach could also be used in other fields where pattern recognition is important, such as medical imaging or astrophysics. The researchers’ code and data will be made available online, allowing other scientists to build upon their work and explore new applications for neural networks in FT spectroscopy.
The development of this method represents a significant step forward in the automation of spectral line detection, and it’s likely to have a lasting impact on the field of atomic spectroscopy.
Cite this article: “Neural Network-Based Spectral Line Detection in Atomic Emission Spectra”, The Science Archive, 2025.
Neural Networks, Fourier Transform Spectroscopy, Atomic Emission Spectra, Spectral Lines, Deep Learning, Bidirectional Lstm-Fcnn, Nickel, Neodymium, Instrumental Resolution-Limited Ringing, Pattern Recognition
