Bayesian least squares deconvolution

Asensio Ramos, A.; Petit, P.
Bibliographical reference

Astronomy and Astrophysics, Volume 583, id.A51, 8 pp.

Advertised on:
11
2015
Number of authors
2
IAC number of authors
1
Citations
10
Refereed citations
10
Description
Aims: We develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods: We consider LSD under the Bayesian framework and we introduce a flexible Gaussian process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results: We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.
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Magnetism, Polarization and Radiative Transfer in Astrophysics
Magnetic fields pervade all astrophysical plasmas and govern most of the variability in the Universe at intermediate time scales. They are present in stars across the whole Hertzsprung-Russell diagram, in galaxies, and even perhaps in the intergalactic medium. Polarized light provides the most reliable source of information at our disposal for the
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