  Precise reconstruction of top quark properties is a challenging task at the
Large Hadron Collider due to combinatorial backgrounds and missing information.
We introduce a physics-informed neural network architecture called the
Covariant Particle Transformer (CPT) for directly predicting the top quark
kinematic properties from reconstructed final state objects. This approach is
permutation invariant and partially Lorentz covariant and can account for a
variable number of input objects. In contrast to previous machine
learning-based reconstruction methods, CPT is able to predict top quark
four-momenta regardless of the jet multiplicity in the event. Using
simulations, we show that the CPT performs favorably compared with other
machine learning top quark reconstruction approaches.
