Accelerated Fitting of Stellar Spectra

Abstract

Stellar spectra are often modeled and fit by interpolating within a rectilinear grid of synthetic spectra to derive the stars' labels: stellar parameters and elemental abundances. However, the number of synthetic spectra needed for a rectilinear grid grows exponentially with the label space dimensions, precluding the simultaneous and self-consistent fitting of more than a few elemental abundances. Shortcuts such as fitting subsets of labels separately can introduce unknown systematics and do not produce correct error covariances in the derived labels. In this paper we present a new approach -- CHAT (Convex Hull Adaptive Tessellation) -- which includes several new ideas for inexpensively generating a sufficient stellar synthetic library, using linear algebra and the concept of an adaptive, data-driven grid. A convex hull approximates the region where the data lie in the label space. A variety of tests with mock datasets demonstrate that CHAT can reduce the number of required synthetic model calculations by three orders of magnitude in an 8D label space. The reduction will be even larger for higher-dimensional label spaces. In CHAT the computational effort increases only linearly with the number of labels that are fit simultaneously. Around each of these grid points in label space an approximate synthetic spectrum can be generated through linear expansion using a set of "gradient spectra" that represent flux derivatives at every wavelength point with respect to all labels. These techniques provide new opportunities to fit the full stellar spectra from large surveys with 15-30 labels simultaneously.