with higher numerical resolutions lead to slower onset of the shock revival in both our 3D and 2D models. This feature is qualitatively consistent with the 2D self-consistent models by [@marek] and with the 3D idealized models by [@hanke13; @couch13], respectively.

$\sigma_{400}$ in Table 1 represents the model dispersion of $t_{400}$, which varies much more stochastically in 3D models with different numerical resolution (from 1.2 to 24 ms) than those in 2D (from 6 to 10 ms). For model 3D-H-3, the shock revival is most delayed (e.g., Table 1) and the shock expansion is weakest among the computed models (dotted red line in Figure \[fig1\]). @nagakura13 proposed that shock revival is very sensitive to the imposed seed perturbations near the stalled shock. Following the hypothesis, we speculate that the influence of seed perturbations is seen most remarkably in our weakest explosion model.

$E_{\rm diag}$ in Table 1 denotes the diagnostic energy defined as the total energy (internal plus kinetic plus gravitational), integrated over all matter where the sum of the corresponding specific energies is positive. We include recombination energy in internal energy([@bruenn13]). Reflecting the earlier shock revival, the diagnostic energy is systematically bigger in 2D than in 3D. These diagnostic 