2019AJ....157..243X

Continuum normalization of echelle spectra is an important data analysis step that is difficult to automate. Polynomial fitting requires a reasonably high-order model to follow the steep slope of the blaze function. However, in the presence of deep spectral lines, a high-order polynomial fit can result in ripples in the normalized continuum that increase errors in spectral analysis. Here, we present two algorithms for flattening the spectrum continuum. The Alpha-shape Fitting to Spectrum algorithm is completely data driven, using an alpha shape to obtain an initial estimate of the blaze function. The Alpha-shape and Lab Source Fitting to Spectrum algorithm incorporates a continuum constraint from a laboratory source reference spectrum for the blaze function estimation. These algorithms are tested on a simulated spectrum, where we demonstrate improved normalization compared to polynomial regression for continuum fitting. We show an additional application, using the algorithms for mitigation of spatially correlated quantum efficiency variations and fringing in the charge-coupled device detector of the EXtreme PREcision Spectrometer.