A neural network trained only on simulated spectra detects weak, blended, and ringing-distorted lines in experimental FT atomic spectra, recovering roughly 95% of human-found lines and enabling two new Ni II level identifications.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.atom-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A neural network approach for line detection in complex atomic emission spectra measured by high-resolution Fourier transform spectroscopy
A neural network trained only on simulated spectra detects weak, blended, and ringing-distorted lines in experimental FT atomic spectra, recovering roughly 95% of human-found lines and enabling two new Ni II level identifications.