Doppler Tomography by Total Variation Minimization

TL;DR

This paper introduces a Doppler tomography method using total variation minimization that effectively reconstructs sharp, localized features in Doppler maps, especially with limited data, and demonstrates high precision on real astronomical data.

Contribution

The paper presents a novel Doppler tomography model based on total variation minimization, improving reconstruction of sharp features and reducing dependence on hyperparameters.

Findings

High-precision reproduction of observed spectra.

Better reconstruction of sharp edges compared to maximum entropy methods.

Hyperparameter estimation via cross-validation enhances model robustness.

Abstract

We have developed a new model of the Doppler tomography using total variation minimization (DTTVM). This method can reconstruct localized and non-axisymmetric profiles having sharp edges in the Doppler map. This characteristic is emphasized in the case that the number of the input data is small. We apply this model to real data of the dwarf novae, WZ Sge in superoutburst and TU Men in quiescence. We confirmed that DTTVM can reproduce the observed spectra with a high precision. Compared with the models based on the maximum entropy method, DTTVM provides the Doppler maps that little depend on the hyper-parameter and on the presence of the absorption core. We also introduce a cross-validation method to estimate reasonable values of a hyperparameter in the model by the data itself.