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Simulations, Data Analysis and Algorithms

Figure 2 | Computational Astrophysics and Cosmology

Figure 2

From: Machine learning applied to simulations of collisions between rotating, differentiated planets

Figure 2

Diversity of collision outcomes. The images above show the outcomes for a subset of the collisions in the 12D_LHS200 dataset. The images are ordered by their impact geometry. From left to right, the impact parameter (\(b_{\infty }\)) increases from head-on (\(b_{\infty } = 0\)) to grazing impacts (\(b_{\infty } \rightarrow 1\)). From bottom to top, the relative asymptotic velocity increases (\(v_{\infty }\)). Thus, collisions near the top left are high-velocity, head-on impacts, whereas the collisions near the lower right are low-velocity, grazing collisions. Head-on, high velocity impacts are catastrophically disruptive to both the target and projectile, whereas grazing collisions tend to result in hit-and-run outcomes. At lower velocities, the target and projectile tend to merge and form a single remnant. In all collisions, debris is generated. The spatial distribution of this debris is strongly dependent on the collision geometry. Emulators must be able to accurately predict post-impact properties for a wide range of collision outcomes. The color scale indicates log-density

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