Abstract
This paper presents an automated pipeline for wavelength calibration of slit-based astronomical spectra, combining robust peak-matching techniques with a lightweight preprocessing step. The method is based on initial peak alignment using Dynamic Time Warping (DTW), followed by refinement of the dispersion solution with the Random Sample Consensus (RANSAC) algorithm. To ensure consistency between observed and reference spectra, a compact one-dimensional convolutional neural network (CNN) is employed at the preprocessing stage to identify the spectral range and corresponding diffraction grating. This step enables the selection of an appropriate reference spectrum but does not directly affect the calibration procedure. The DTW-based approach provides reliable initial correspondences between spectral peaks under nonlinear distortions and noise, while RANSAC filtering removes incorrect matches and yields a robust polynomial dispersion solution. The method is validated on real observational data from calibration lamps and demonstrates stable performance, high matching accuracy, and full automation capability. Compared to traditional manual or semi-automatic approaches, the proposed pipeline significantly reduces processing time while maintaining calibration precision. The developed solution is planned to be deployed as a service in the virtual observatory infrastructure and is applicable for processing numerous spectral data.