The study of stellar surfaces can reveal information about the chemical composition, interior structure, and magnetic properties of stars. It is also critical to the detection and characterization of extrasolar planets, in particular those targeted in extreme precision radial velocity (EPRV) searches, which must contend with stellar variability that is often orders of magnitude stronger than the planetary signal. One of the most successful methods to map the surfaces of stars is Doppler imaging, in which the presence of inhomogeneities is inferred from subtle line shape changes in high resolution stellar spectra. In this paper, we present a novel, efficient, and closed-form solution to the problem of Doppler imaging of stellar surfaces. Our model explicitly allows for incomplete knowledge of the local (rest frame) stellar spectrum, allowing one to learn differences from spectral templates while simultaneously mapping the stellar surface. It therefore works on blended lines, regions of the spectrum where line formation mechanisms are not well understood, or stars whose spots have intrinsically different spectra from the rest of the photosphere. We implement the model within the open source starry framework, making it fast, differentiable, and easy to use in both optimization and posterior inference settings. As a proof-of-concept, we use our model to infer the surface map of the brown dwarf WISE 1049-5319B, finding close agreement with the solution of Crossfield et al. (2014). We also discuss Doppler imaging in the context of EPRV studies and describe an interpretable spectral-temporal Gaussian process for stellar spectral variability that we expect will be important for EPRV exoplanet searches.
- Pub Date:
- October 2021
- Astrophysics - Solar and Stellar Astrophysics;
- Astrophysics - Earth and Planetary Astrophysics;
- Astrophysics - Instrumentation and Methods for Astrophysics
- 59 pages, 18 figures. Prepared using the showyourwork (github.com/rodluger/showyourwork) open source scientific article workflow