Doppler tomography by total variation minimization

M. Uemura, T. Kato, D. Nogami, and R. Mennickent, “Doppler tomography by total variation minimization,” PASJ, vol. 67, p. 22, 2015

We have developed a new model of Doppler tomography using total variation minimization (DTTVM). This method can reconstruct localized and nonaxisymmetric profiles with sharp edges in the Doppler map. This characteristic is emphasized in the case where input data are small in number. We apply this model to natural data for the dwarf nova WZ Sge in superoutburst and TU Men in quiescence. We confirm that DTTVM can reproduce the observed spectra with high precision. Compared with the models based on the maximum entropy method, our new model can provide Doppler maps that little depend on the hyperparameter and on the presence of the absorption core. We also introduce a cross-validation method of estimating reasonable values of a hyperparameter in the model from the data themselves.