Speaker
Description
Continuum normalization is a crucial but often underappreciated step in stellar spectroscopy. Errors in continuum placement bias comparisons between observed and synthetic spectra, and can propagate into stellar parameters, chemical abundances, and radial velocities. They are especially problematic for wide spectral features, where coherent normalization residuals can mimic or obscure astrophysical signal. Despite this, normalization often remains survey-specific, difficult to scale, and partly dependent on manual intervention.
Data-driven tools such as SUPPNet have shown the promise of automatic normalization, but are usually limited to specific spectral resolutions, wavelength ranges, or already-merged one-dimensional spectra. I present a new transformer-based normalization model designed for more heterogeneous stellar data, including both merged spectra and individual echelle orders. Given a measured spectrum, the model predicts the corresponding pseudo-continuum, enabling normalized spectra to be obtained in a consistent, reproducible way. It is trained to be robust across spectral resolution, signal-to-noise ratio, and spectral morphology, including absorption-dominated spectra, emission-line spectra, and challenging continuum shapes.
The goal is to make normalization a reusable and reproducible component of stellar spectral analysis rather than a fragile preprocessing step tied to a particular survey or instrument. The tool is being developed as a Python library with a command-line interface, and an MCP-compatible interface for integration into AI-assisted spectroscopic workflows.