An automated procedure for measurement of prominence transverse velocities

Abstract

A computer algorithm for measurment of transverse velocities (proper motion) in prominences has been developed. We present the method and examples of computed proper motion maps. The method is a modified version of the local cross correlation technique previously applied to granulation images (November 1986, Title et al 1987, November and Simon 1988, November 1988, Darvann 1988, Brandt et al 1988).

Prominence images show much steeper intensity gradients and a wider range of spatial scales of fine structure than granulation images. Due to this we find it necessary to replace the prominence images by an image showing the intensity gradients (derivative of the intensity image). Furthermore, in our algorithm we compute absolute differences instead of correlation coefficients in order to reduce the influence of large scale intensity gradients across a local window (Karud 1988). We have tested the method on datasets obtained at the Vacuum Tower Telescope of NSO/SP. The accuracy of the algorithm is seen to be ±0.3 pixels which, in our data, corresponds to about 1/10 arcsec. Seeing effects are effectively reduced by averaging N cross correlation functions formed from images sampled Δt apart. We find that Δt = 120s gives the highest accuracy in the proper motion measurement when applied to our data consisting of quiescent prominences. The correlation coefficient between two interlaced, independent proper motion maps is as high as 0.92 when N=50. The size of the smallest structure for which a proper motion velocity can be measured is limited by the size of the smallest local window that can successfully be applied in the measurement. It needs to be large enough to contain some high contrast structures, typically 4×4 arcsec in our data. Our algorithm is “self-adaptive” to the data in the sense that the window size is changed automatically depending on the presence of local high contrast structures. We conclude that the method successfully produces prominence proper motion maps in addition to being able to correlation track prominence images. Furthermore the algorithm will be useful for destretching of prominence images before producing Doppler-grams or carrying out oscillation studies at high spatial resolution.