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ABSTRACT We use the magnetic field components measured by Zeeman Doppler imaging (ZDI) to calculate the stellar surface force and torque due to magnetic stresses for the fast rotators σ Ori E, 36 Lyn, and CU Vir, and the slow rotator τ Sco. If we assume the stars have spherical photospheres, the estimated torques give spin-down time-scales no larger than 7 × 105 yr. For σ Ori E, the predicted spin-down time-scale, ≃ 6000 yr, is much less than the observationally measured time-scale of ≃ 106 yr. However, for CU Vir, we find that the spin-down time-scale from its ZDI map is 7 × 105 yr in good agreement with its average rate of spin-down from 1960 to 2010. With the exception of τ Sco, the net force due to magnetic stresses at the stellar surface are large compared to the surface-integrated pressure. We discuss possible reasons for the large values of the forces (and torques), and suggest that the likely explanation is that rotation and the magnetic stresses create significant departures from spherical symmetry. 

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.