Comparing explodability predictions from a parameter-optimized semi-analytic model with structure-based progenitor criteria
Three-dimensional (3D) simulations of neutrino-driven core-collapse supernovae are among the most reliable tools for predicting explosion outcome. However, their high computational cost limits systematic surveys over large progenitor samples. We test how well a fast one-dimensional (1D) approach captures progenitor explodability. We use a parameter-optimized semi-analytic 1D explosion model based on Müller et al. (2016), calibrated to the 3D results of Burrows et al. (2024) as an adopted reference set. We compare the model's explodability predictions with commonly used structure-based criteria: compactness, the free-fall mass coordinate, and the two-parameter $μ_4$-$M_4$ criterion. Our analysis shows that the semi-analytic model can reproduce the trends seen in this adopted 3D calibration set by adjusting physically meaningful parameters. This provides a more direct way to examine the physics that controls explodability than traditional structure-based criteria. We identify where the semi-analytic model agrees with these criteria, where it differs, and which physical trends explain the differences. This work clarifies the strengths and limitations of structure-based explodability criteria by evaluating them against a parameter-optimized, neutrino-driven semi-analytic model.