Inferring photospheric horizontal flows from multiple observations with SUVEL models

Photospheric horizontal velocity fields play essential roles in the formation and evolution of numerous solar activities. Various methods for estimating the horizontal velocity field have been proposed in the past. Aiming at the highest available (and future) spatial resolution (10 km pixel−1) observations, a new method, the Shallow U-net models (SUVEL), based on realistic numerical simulation and machine learning techniques, was recently developed to track the photospheric horizontal velocity fields. Although SUVEL has been tested on numerical simulation data, its performance on solar observational data remained unclear. In this work, we apply SUVEL to the photospheric intensity observations from four ground-based solar telescopes (DKIST, GST, NVST, and SST) with the largest available apertures, and compare the results obtained from SUVEL with the Fourier local correlation tracking method (FLCT). Average correlation indices between granular regions and velocity fields inferred by SUVEL (FLCT) are 0.63, 0.81, 0.80, and 0.87 (0.00, 0.11, 0.16, and 0.10) for DKIST, GST, NVST, and SST observations. Higher correlation indices between the velocity fields tracked by SUVEL and granular patterns than FLCT reveal the superior performance of SUVEL, validating its reliability with respect to solar observational data.