Amortized Simulation-Based Inference of Colliding-Wind Binaries from Short, Noisy Image Time Series
Niklas Knöll, Tobias Buck, Lorenzo Branca, Giuseppe Viterbo
astro-ph.SR, astro-ph.IM
Submitted: 2026-06-09
Comments: Accepted to the International Conference for Machine Learning (ICML) workshop AI4Physics 2026
Code: https://github.com/n-knoell/SBI-for-CWBs
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology
- Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
- A COMPASS to Model Comparison and Simulation-Based Inference in Galactic Chemical Evolution
- Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
- Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation
- JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
- Emulating Radiative Transfer in Astrophysical Environments
- Differentiable Conservative Radially Symmetric Fluid Simulations and Stellar Winds -- jf1uids
- Validating Bayesian Inference Algorithms with Simulation-Based Calibration
- CASBI -- Chemical Abundance Simulation-Based Inference for Galactic Archeology
- Flow Matching for Scalable Simulation-Based Inference
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