  Spin-orbit torque characterizations on magnetic heterostructures with
perpendicular anisotropy are demonstrated on a projected vector field magnet
via hysteresis loop shift measurement and harmonic Hall measurement with planar
Hall correction. Accurate magnetic field calibration of the vector magnet is
realized with the help of deep learning models, which are able to capture the
nonlinear behavior between the generated magnetic field and the currents
applied to the magnet. The trained models can successfully predict the applied
current combinations under the circumstances of magnetic field scans, angle
scans, and hysteresis loop shift measurements. The validity of the models is
further verified, complemented by the comparison of the spin-orbit torque
characterization results obtained from the deep-learning-trained vector magnet
system with those obtained from a conventional setup comprised of two separated
electromagnets. The damping-like spin-orbit torque (DL-SOT) efficiencies
(|$\xi_{DL}$|) extracted from the vector magnet and the traditional measurement
configuration are consistent, where |$\xi_{DL}$| $\approx$ 0.22 for amorphous W
and |$\xi_{DL}$| $\approx$ 0.02 for $\alpha$-W. Our work provides an advanced
method to meticulously control a vector magnet and to conveniently perform
various spin-orbit torque characterizations.
