DeepVel: deep learning for the estimation of horizontal velocities at the solar surface
This work addresses the challenge of measuring plasma motions in solar photosphere phenomena for solar physics researchers, representing an incremental improvement by applying deep learning to an existing bottleneck.
The authors tackled the problem of estimating horizontal velocities at the solar surface, which are not directly accessible via spectroscopy, by developing DeepVel, a deep neural network that uses two consecutive continuum images to estimate velocities at every pixel, time step, and three atmospheric heights, achieving results similar to traditional local correlation tracking methods.
Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line-of-sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel and at every time step and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time- and spatially-averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations.