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Paper: Six Dimensional Streaming Algorithm for Cluster Finding in N-Body Simulations
Volume: 527, Astronomical Data Analysis Software and Systems XXIX
Page: 155
Authors: Lemson, G.; Reilly, A.; Ivkin, N.; Braverman, V.; Szalay, A.
Abstract: 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. (2015) showed the connection between cluster detection and memory-efficient streaming algorithms and presented a halo finder based on the heavy hitter algorithm. Later, Ivkin et al. (2018) improved the scalability of the suggested streaming halo finder with an 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 the streaming method we introduce. We then demonstrate a use of this suggested method, which allows one to find the same halos as before, whilst also detecting those which were indistinguishable using prior methods.
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