Deep-learning about horizontal velocities at the solar surface

The velocity fields are of great importance for understanding dynamics and structure of the solar atmosphere. The line of sight velocities are coded in the wavelength shifts of the spectral lines, thanks to the Doppler effect, and relatively easy to measure. On the other hand, the orthogonal ("horizontal") components of the velocity vector are impossible to measure directly.

The most popular method for estimating the horizontal velocities is so-called local correlation tracking (LCT, November & Simon, 1988). It is based on comparing successive images of the solar surface in the continuum light and transforming their differences into information about the horizontal fields. However, the LCT algorithm suffers from several limitations.

In a paper by Andres Asensio Ramos and Iker S. Requerey (with a small contribution from my side) accepted by A&A and published on Arxiv some weeks ago (2017arXiv170305128A) this problem is tackled by the deep-learning approach. A deep fully convolutional neural network is trained on synthetic observations from 3D MHD simulations of the solar photosphere and then applied to the real observation with the IMaX instrument on board the SUNRISE balloon (Martinez Pillet et al, 2011; Solanki, 2010). The method is validated using simulation snapshots of the quiet sun produced with the MANCHA code that I have been developing in the last couple of years.