Metadata-Version: 2.5
Name: NeuronStar
Version: 0.0.2
Summary: NeuronStar: fast neural inference for neutron-star matter
Project-URL: Repository, https://github.com/prashantstar123/NeuronStar
Project-URL: Issues, https://github.com/prashantstar123/NeuronStar/issues
Project-URL: Paper, https://arxiv.org/abs/2610.05121
Author: Prashant Thakur
License: MIT
License-File: LICENSE
Keywords: Bayesian evidence,equation of state,neural posterior estimation,neutron star,simulation-based inference
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.9
Description-Content-Type: text/markdown

# NeuronStar

**Fast neural inference for neutron-star matter.**

NeuronStar is the software of the paper *Fast Bayesian Updating of the Neutron-Star Equation of State with
Neural Posterior and Evidence Estimation* by Prashant Thakur (Department of Physics, Yonsei University),
[arXiv:2610.05121](https://arxiv.org/abs/2610.05121).
It provides amortized neural posterior estimation with importance-sampling correction (A-NET+IS), truncated
sequential neural posterior estimation (TSNPE+MIS), and the Green-function Evidence Network (EN) for
equation-of-state inference with nuclear, NICER, GW170817, and pQCD constraints.

**Code:** the full code and data, with step-by-step instructions to reproduce the paper, are on GitHub:
https://github.com/prashantstar123/NeuronStar. This PyPI release holds the package name and does not yet
contain the code, so please use the GitHub repository. Contributions (issues and pull requests) are welcome there.

## Citation

```bibtex
@article{Thakur:2026smp,
    author = "Thakur, Prashant",
    title = "{Fast Bayesian Updating of the Neutron-Star Equation of State with Neural Posterior and Evidence Estimation}",
    eprint = "2610.05121",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.HE",
    month = "10",
    year = "2026"
}
```
