  Collective motion of locally interacting agents is found ubiquitously
throughout nature. The inability to probe individuals has driven longstanding
interest in the development of methods for inferring the underlying
interactions. In the context of heterogeneous collectives, where the population
consists of individuals driven by different interactions, existing approaches
require some knowledge about the heterogeneities or underlying interactions.
Here, we investigate the feasibility of identifying the identities in a
heterogeneous collective without such prior knowledge. We numerically explore
the behavior of a heterogeneous Vicsek model and find sufficiently long
trajectories intrinsically cluster in a PCA-based dimensionally reduced
model-agnostic description of the data. We identify how heterogeneities in each
parameter in the model (interaction radius, noise, population proportions)
dictate this clustering. Finally, we show the generality of this phenomenon by
finding similar behavior in a heterogeneous D'Orsogna model. Altogether, our
results establish and quantify the intrinsic model-agnostic statistical
disentanglement of identities in heterogeneous collectives.
