  In tokamak operations, accurate equilibrium reconstruction is essential for
reliable real-time control and realistic post-shot instability analysis. The
safety factor (q) profile defines the magnetic field line pitch angle, which is
the central element in equilibrium reconstruction. The motional Stark effect
(MSE) diagnostic has been a standard measurement for the magnetic field line
pitch angle in tokamaks that are equipped with neutral beams. However, the MSE
data are not always available due to experimental constraints, especially in
future devices without neutral beams. Here we develop a deep learning-based
surrogate model of the gyrokinetic toroidal code for q profile reconstruction
(SGTC-QR) that can reconstruct the q profile with the measurements without MSE
to mimic the traditional equilibrium reconstruction with the MSE constraint.
The model demonstrates promising performance, and the sub-millisecond inference
time is compatible with the real-time plasma control system.
