  Recently, Kolmogorov-Arnold Networks (KANs) have been proposed as an
alternative to multilayer perceptrons, suggesting advantages in performance and
interpretability. We study a typical binary event classification task in
high-energy physics including high-level features and comment on the
performance and interpretability of KANs in this context. We find that the
learned activation functions of a one-layer KAN resemble the log-likelihood
ratio of the input features. In deeper KANs, the activations in the first KAN
layer differ from those in the one-layer KAN, which indicates that the deeper
KANs learn more complex representations of the data. We study KANs with
different depths and widths and we compare them to multilayer perceptrons in
terms of performance and number of trainable parameters. For the chosen
classification task, we do not find that KANs are more parameter efficient.
However, small KANs may offer advantages in terms of interpretability that come
at the cost of only a moderate loss in performance.
