Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing

Documenti analoghi

• LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators
  di: Filipp, Andreas, et al.
  Pubblicazione: (2026)
• Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis
  di: Legin, Ronan, et al.
  Pubblicazione: (2025)
• Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
  di: Barco, Gabriel Missael, et al.
  Pubblicazione: (2025)
• Neural Deprojection of Galaxy Stellar Mass Profiles
  di: Yantovski-Barth, M. J., et al.
  Pubblicazione: (2025)
• Transformer Embeddings for Fast Microlensing Inference
  di: Smyth, Nolan, et al.
  Pubblicazione: (2025)