  A good feature representation is a determinant factor to achieve high
performance for many machine learning algorithms in terms of classification.
This is especially true for techniques that do not build complex internal
representations of data (e.g. decision trees, in contrast to deep neural
networks). To transform the feature space, feature construction techniques
build new high-level features from the original ones. Among these techniques,
Genetic Programming is a good candidate to provide interpretable features
required for data analysis in high energy physics. Classically, original
features or higher-level features based on physics first principles are used as
inputs for training. However, physicists would benefit from an automatic and
interpretable feature construction for the classification of particle collision
events.
  Our main contribution consists in combining different aspects of Genetic
Programming and applying them to feature construction for experimental physics.
In particular, to be applicable to physics, dimensional consistency is enforced
using grammars.
  Results of experiments on three physics datasets show that the constructed
features can bring a significant gain to the classification accuracy. To the
best of our knowledge, it is the first time a method is proposed for
interpretable feature construction with units of measurement, and that experts
in high-energy physics validate the overall approach as well as the
interpretability of the built features.
