VERSUS is a void-finding algorithm that identifies spherical underdensities matching excursion-set predictions for the void size function, validated on synthetic particles and AbacusSummit mocks with realistic galaxy populations.
and Dolag, Klaus , year=
2 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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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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VERSUS: An excursion-set-inspired void-finder for the Stage-IV era
VERSUS is a void-finding algorithm that identifies spherical underdensities matching excursion-set predictions for the void size function, validated on synthetic particles and AbacusSummit mocks with realistic galaxy populations.
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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.