  We train a novel deep learning architecture to perform likelihood-free
inference on the value of the cosmological parameters from halo catalogs of the
Quijote N-body simulations. Our model takes as input a halo catalog where each
halo is characterized by its position, mass, and velocity moduli. By
construction, our model is E(3) invariant and is designed to extract
information hierarchically. Unlike graph neural networks, it does not require
the transformation of the input halo (or galaxy) catalog into a graph. Given
its simplicity, our model can process point clouds with large numbers of
points. We discuss the advantages of this class of methods but also point out
its limitations and potential ways to improve them for cosmological data.
