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Continuum normalization of echelle spectra is an important data analysis stepthat is difficult to automate. Polynomial fitting requires a reasonably highorder model to follow the steep slope of the blaze function. However, in thepresence of deep spectral lines, a high order polynomial fit can result inripples in the normalized continuum that increase errors in spectral analysis.Here, we present two algorithms for flattening the spectrum continuum. TheAlpha-shape Fitting to Spectrum algorithm (AFS) is completely data-driven,using an alpha shape to obtain an initial estimate of the blaze function. TheAlpha-shape and Lab Source Fitting to Spectrum algorithm (ALSFS) incorporates acontinuum constraint from a lab source reference spectrum for the blazefunction estimation. These algorithms are tested on a simulated spectrum, wherewe demonstrate improved normalization compared to polynomial regression forcontinuum fitting. We show an additional application, using the algorithms formitigation of spatially correlated quantum efficiency variations and fringingin the CCD detector of the EXtreme PREcision Spectrometer (EXPRES).

Modeling the Echelle Spectra Continuum with Alpha Shapes and Local Regression Fitting