  Machine learning has the potential to aid our understanding of phase
structures in lattice quantum field theories through the statistical analysis
of Monte Carlo samples. Available algorithms, in particular those based on deep
learning, often demonstrate remarkable performance in the search for previously
unidentified features, but tend to lack transparency if applied naively. To
address these shortcomings, we propose representation learning in combination
with interpretability methods as a framework for the identification of
observables. More specifically, we investigate action parameter regression as a
pretext task while using layer-wise relevance propagation (LRP) to identify the
most important observables depending on the location in the phase diagram. The
approach is put to work in the context of a scalar Yukawa model in (2+1)d.
First, we investigate a multilayer perceptron to determine an importance
hierarchy of several predefined, standard observables. The method is then
applied directly to the raw field configurations using a convolutional network,
demonstrating the ability to reconstruct all order parameters from the learned
filter weights. Based on our results, we argue that due to its broad
applicability, attribution methods such as LRP could prove a useful and
versatile tool in our search for new physical insights. In the case of the
Yukawa model, it facilitates the construction of an observable that
characterises the symmetric phase.
