nmma 0.2.0

A nuclear physics multi-messenger Bayesian inference library

NMMA

a pythonic library for probing nuclear physics and cosmology with multimessenger analysis

Read our official documentation: NMMA Documentation

Check out our contribution guide: For contributors

A tutorial on how to produce simulations of lightcurves is given here tutorial-lightcurve_simulation.ipynb

Citing NMMA

When utilizing this code for a publication, kindly make a reference to the package by its name, NMMA, and a citation to the companion paper An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers. The BibTeX entry for the paper is:

@article{Pang:2022rzc,
      title={An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers},
      author={Peter T. H. Pang and Tim Dietrich and Michael W. Coughlin and Mattia Bulla and Ingo Tews and Mouza Almualla and Tyler Barna and Weizmann Kiendrebeogo and Nina Kunert and Gargi Mansingh and Brandon Reed and Niharika Sravan and Andrew Toivonen and Sarah Antier and Robert O. VandenBerg and Jack Heinzel and Vsevolod Nedora and Pouyan Salehi and Ritwik Sharma and Rahul Somasundaram and Chris Van Den Broeck},
      journal={Nature Communications},
      year={2023},
      month={Dec},
      day={20},
      volume={14},
      number={1},
      pages={8352},
      issn={2041-1723},
      doi={10.1038/s41467-023-43932-6},
      url={https://doi.org/10.1038/s41467-023-43932-6}
}

Acknowledgments

If you benefited from participating in our community, we ask that you please acknowledge the Nuclear Multi-Messenger Astronomy collaboration, and particular individuals who helped you, in any publications. Please use the following text for this acknowledgment:

We acknowledge the Nuclear Multi-Messenger Astronomy collective as an open community of multi-domain experts and collaborators. This community and <names of individuals>, in particular, were important for the development of this project.

Funding

We gratefully acknowledge previous and current support from the U.S. National Science Foundation (NSF) Harnessing the Data Revolution (HDR) Institute for Accelerating AI Algorithms for Data Driven Discovery (A3D3) under Cooperative Agreement No. PHY-2117997 and the European Research Council (ERC) under the European Union's Starting Grant (Grant No. 101076369).