CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference
Haibin Ren, Wei Zhu
astro-ph.IM, astro-ph.EP, astro-ph.GA, astro-ph.SR
Submitted: 2026-07-06
Comments: 17 pages, 8 figures, submitted to AJ
Code: https://github.com/anh-tong/signax
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- ET White Paper: To Find the First Earth 2.0
- Candidate Microlensing Brown Dwarfs in Binary Lens Systems from the 2023--2025 Observing Seasons
- Simulation-Based Inference: A Practical Guide
- Galactic microlensing with rotating binaries
- The binary gravitational lens and its extreme cases
- On Neural Differential Equations
- A Generalised Signature Method for Multivariate Time Series Feature Extraction
- Neural Controlled Differential Equations for Online Prediction Tasks
- Transformer Embeddings for Fast Microlensing Inference
- An HST Wide Field Survey of the Galactic Bulge: Overview, Strategy, and First Results
- OGLE-IV: Fourth Phase of the Optical Gravitational Lensing Experiment
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