Simulation-based Deep Sets model with neural posterior estimation halves scatter in cluster mass estimates from galaxy kinematics compared to the M-sigma relation.
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The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
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Cluster Mass Inference from Galaxy Kinematics
Simulation-based Deep Sets model with neural posterior estimation halves scatter in cluster mass estimates from galaxy kinematics compared to the M-sigma relation.
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.