  Recent proposals have suggested the use of supervised learning with
convolutional neural networks to shed light on some of the less well known
phases of the Fermi-Hubbard model through the classification of snapshots from
the quantum gas microscopy of ultracold atoms in optical lattices. However,
there have been challenges in the interpretability of networks with more than
one convolutional filter coupled to the input images. Here, we expand on
previous work by considering multiple filters in the first convolutional layer
and developing a process for analyzing the physical relevance of patterns
obtained in the trained filters. We benchmark our approach at half-filling,
where strong antiferromagnetic correlations are known to be present, and we
find that upon hole doping, previously unknown patterns arise at temperatures
below the tunneling amplitude. These patterns may be a signature of interesting
arrangements of fermions in the lattice.
