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Inferring photospheric horizontal flows from multiple observations with SUVEL models

Quan Xie, Jiajia Liu, Robert Erdélyi, Yuming Wang

TL;DR

This study validates the SUVEL shallow U-net framework for inferring solar photospheric horizontal velocity fields on real observational data from four ground-based telescopes (DKIST, GST, NVST, SST) by comparing against the traditional FLCT method. SUVEL demonstrates higher alignment with granulation patterns and divergence structures, yielding larger correlation with granules (CI up to 0.87) and more reliable velocity fields across datasets. When combined with Optimized ASDA for vortex detection, SUVEL detects significantly more vortices and resolves smaller-scale motions than FLCT, highlighting its potential for detailed photospheric dynamics. The SUVEL package is open-source, enabling broader adoption and future work on vortex lifetimes and small-scale structure handling in velocity reconstructions.

Abstract

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) 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.

Inferring photospheric horizontal flows from multiple observations with SUVEL models

TL;DR

This study validates the SUVEL shallow U-net framework for inferring solar photospheric horizontal velocity fields on real observational data from four ground-based telescopes (DKIST, GST, NVST, SST) by comparing against the traditional FLCT method. SUVEL demonstrates higher alignment with granulation patterns and divergence structures, yielding larger correlation with granules (CI up to 0.87) and more reliable velocity fields across datasets. When combined with Optimized ASDA for vortex detection, SUVEL detects significantly more vortices and resolves smaller-scale motions than FLCT, highlighting its potential for detailed photospheric dynamics. The SUVEL package is open-source, enabling broader adoption and future work on vortex lifetimes and small-scale structure handling in velocity reconstructions.

Abstract

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) 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.
Paper Structure (6 sections, 5 equations, 4 figures, 1 table)

This paper contains 6 sections, 5 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods and observations
  3. Results
  4. Granules and velocity fields
  5. Influence on vortex detections
  6. Conclusion and discussions

Figures (4)

  • Figure 1: The four red squares drawn on the SDO/HMI magnetograms in panels (a1)-(d1) depict the FOV locations of DKIST, GST, NVST, and SST observations, respectively. The bottom row shows four example intensity images of an instance in the four observations. The yellow squares outline the regions for further study.
  • Figure 2: (a1)-(d1) Close-up views of the yellow boxes shown in Figure \ref{['fig_obs']}. (a2)-(d2) Green and black dots represent the granules regions and intergranular lanes. (a3)-(d3) The black arrows depict the velocity fields inferred by SUVEL, with the backgrounds showing the corresponding divergence maps. (a4)-(d4) Similar to (a3)-(d3), but for the velocity fields estimated by FLCT. (a5)-(d5) The magnitude maps of velocity fields inferred by SUVEL. (a6)-(d6) Similar to (a5)-(d5), but for the results of FLCT.
  • Figure 3: Per-frame average correlation indices between velocity fields and granulation patterns. CI$_1$ (blue) and CI$_2$ (red) depict the results of granule regions and intergranular lanes, respectively. (a1)-(d1) represent the results of the velocity fields inferred by SUVEL. (a2)-(d2) depict the results from FLCT.
  • Figure 4: Per-frame average number of detected vortices. Subscripts p and n (blue and red) denote positive and negative vortices, respectively. Black curves and texts are the results of all swirls. $\sigma$ represents the standard deviation. (a1)-(d1) Number of vortices detected by the Optimized ASDA and SUVEL from the DKIST, GST, NVST, and SST observations. (a2)-(d2) Similar to the top panels, but for the results of FLCT.