  Active matter systems being in a non-equilibrium state, exhibit complex
behaviors such as self-organization and giving rise to emergent phenomena.
There are many examples of active particles with biological origins, including
bacteria and spermatozoa, or with artificial origins, such as self-propelled
swimmers and Janus particles. The ability to manipulate active particles is
vital for their effective application e.g. separating motile spermatozoa from
nonmotile and dead ones, to increase fertilization chance. In this study, we
proposed a mechanism -- an apparatus -- to sort and demix active particles
based on their motility values (P\`eclet number). Initially, using Brownian
simulations we demonstrated the feasibility of sorting self-propelled
particles. Following this, we employed machine learning methods, supplemented
with data from comprehensive simulations that we conducted for this study, to
model the complex behavior of active particles. This enabled us to sort them
based on their P\`eclet number. Finally, we evaluated the performance of the
developed models and showed their effectiveness in demixing and sorting the
active particles. Our findings can find applications in various fields,
including physics, biology, and biomedical science, where the sorting and
manipulation of active particles play a pivotal role.
