Six Dimensional Streaming Algorithm for Cluster Finding in N-Body
  Simulations
  Cosmological N-body simulations are crucial for understanding how the
Universe evolves. Studying large-scale distributions of matter in these
simulations and comparing them to observations usually involves detecting dense
clusters of particles called "halos,'' which are gravitationally bound and
expected to form galaxies. However, traditional cluster finders are
computationally expensive and use massive amounts of memory. Recent work by Liu
et al (Liu et al. (2015)) showed the connection between cluster detection and
memory-efficient streaming algorithms and presented a halo finder based on
heavy hitter algorithm. Later, Ivkin et al. (Ivkin et al. (2018)) improved the
scalability of suggested streaming halo finder with efficient GPU
implementation. Both works map particles' positions onto a discrete grid, and
therefore lose the rest of the information, such as their velocities.
Therefore, two halos travelling through each other are indistinguishable in
positional space, while the velocity distribution of those halos can help to
identify this process which is worth further studying. In this project we
analyze data from the Millennium Simulation Project (Springel et al. (2005)) to
motivate the inclusion of the velocity into streaming method we introduce. We
then demonstrate a use of suggested method, which allows one to find the same
halos as before, while also detecting those which were indistinguishable in
prior methods.
