An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers
Peter T.H.Pang, Tim Dietrich, Michael W.Coughlin, Mattia Bulla, Ingo, Tews, Mouza Almualla, Tyler Barna, Weizmann Kiendrebeogo, Nina Kunert, Gargi, Mansingh, Brandon Reed, Niharika Sravan, Andrew Toivonen, Sarah Antier,, Robert O. VandenBerg, Jack Heinzel, Vsevolod Nedora

TL;DR
This paper introduces an enhanced computational framework, NMMA, that integrates nuclear physics and multi-messenger astrophysics to analyze neutron star mergers across gravitational waves, electromagnetic signals, and dense matter physics.
Contribution
The paper presents an extension of the NMMA framework for simultaneous analysis of gravitational-wave, kilonova, and gamma-ray burst data from neutron star mergers.
Findings
Estimated neutron star radius: 11.98 km with uncertainties.
Integrated multi-messenger data improves constraints on dense matter.
Demonstrated the framework's capability to combine diverse observational data.
Abstract
The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst GRB170817A, as well as the observed afterglow has delivered a scientific breakthrough. For an accurate interpretation of all these different messengers, one requires robust theoretical models that describe the emitted gravitational-wave, the electromagnetic emission, and dense matter reliably. In addition, one needs efficient and accurate computational tools to ensure a correct cross-correlation between the models and the observational data. For this purpose, we have developed the Nuclear-physics and Multi-Messenger Astrophysics framework NMMA. The code allows incorporation of nuclear-physics constraints at low densities as well as X-ray and radio observations of isolated neutron stars. In previous works, the NMMA code has allowed us to constrain…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
