What is NMMA ?
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A framework for multi-messenger light-curve analysis
NMMA (Nuclear Multi-Messenger Astronomy) is a Python library for Bayesian parameter inference of multi-messenger signals from compact binary mergers. It enables the simultaneous analysis of gravitational-wave (GW) signals, kilonovae (KNe), Supernovae (SNe), and gamma-ray burst (GRB) afterglows, providing constraints on the neutron star equation of state (EOS) and the Hubble constant H0.
NMMA uses Bilby as its Bayesian inference backend and supports samplers including dynesty, PyMultiNest, and UltraNest. It has been applied to the landmark multi-messenger event GW170817 / AT2017gfo / GRB170817A, yielding a neutron star radius estimate of R1.4 = 11.98+0.35-0.40 km. Further details are available in Pang et al. (2023).
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NMMA in Astro-COLIBRI - Supernova light-curve fitting
Within the Astro-COLIBRI platform, NMMA can be used to analyze the photometric light curves of a wide range of transients detected by optical surveys such as the Zwicky Transient Facility (ZTF), the Asteroid Terrestrial-impact Last Alert System (ATLAS), the Legacy Survey of Space and Time (LSST), and others. The goal is to discriminate the nature of a transient by fitting its observed light curve against a set of astrophysical models and computing Bayesian evidence for each.
When a new transient is detected and its photometry uploaded to Astro-COLIBRI, users can request NMMA fits to analyze the light curve using models from the SNCosmo library as well as dedicated templates, covering the following phenomena:- Type Ia (SNIa) — Thermonuclear supernovae from white dwarfs in binary systems exceeding the Chandrasekhar limit. Fitted with SALT3 and nugent-sn1a (Nugent template). SNIa are remarkably homogeneous in luminosity and serve as standard candles for cosmological distance measurements.
- Type IIP / IIL (SNII) — Core-collapse supernovae from massive stars (> 8 M☉) showing prominent hydrogen lines. IIP show a characteristic plateau in their light curve; IIL decline linearly. Fitted with nugent-sn2p and nugent-sn2l.
- Type IIn (SNIIn) — Interacting core-collapse supernovae with narrow hydrogen emission lines, signature of ejecta colliding with a dense circumstellar medium. Fitted with nugent-sn2n.
- Type Ib/c (SNIb/c) - Hypernovae — Stripped-envelope supernovae lacking hydrogen (SNIb) and sometimes helium (SNIc) lines, from massive stars that lost their outer envelopes. Hypernovae are extremely energetic variants associated with long GRBs, calibrated on SN 1998bw. Fitted with nugent-sn1bc and nugent-hyper.
- Type IIb (SNIIb) — Transitional events showing hydrogen lines early on that fade over time, eventually resembling SNIb spectra. A notable example is ZTF21abotose (SN2021ugl), initially ambiguous between kilonova and supernova, later confirmed as a SNIIb by spectroscopy.
- Shock Cooling — Early-time emission from shock-heated extended stellar material, preceding the main supernova peak. Characteristic of Type IIb and IIP supernovae. Fitted with Piro2021.
- Kilonovae (KNe) — Electromagnetic counterparts to binary neutron star (BNS) and neutron-star black hole (NSBH) mergers, powered by r-process nucleosynthesis.
For each model, NMMA computes the Bayes factor — a statistical measure of how well the model explains the data relative to other hypotheses. The model with the highest Bayes factor provides the most likely physical interpretation of the transient. This allows Astro-COLIBRI users to quickly assess the nature of an alert and decide whether to trigger follow-up observations.
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An open-source code
NMMA is open source, publicly available on GitHub and PyPI. It is actively developed by an international collaboration of astrophysicists. You will find NMMA on GitHub/nuclear-multimessenger-astronomy/nmma and PyPI.org/project/nmma. Full documentation is available on ReadTheDocs.
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A service provided by Astro-COLIBRI
The cloud computing resources and the maintenance of the NMMA API are provided by Astro-COLIBRI. The Astro-COLIBRI frontends (web, Android, iOS) provide a convenient interface to submit light-curve fitting jobs and visualize results derived using NMMA.
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dynesty — J. S. Speagle, MNRAS 493, 3132 (2020) DOI, arXiv
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PyMultiNest — J. Buchner et al., A&A 564, A125 (2014) DOI, arXiv
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Systematics error — S. Jhawar et al., Phys. Rev. D 111, 043046 (2025) DOI, arXiv
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Likelihood-free inference — M. Desai et al., MNRAS (2024) DOI, arXiv
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Observing scenarios O4/O5 — R. W. Kiendrebeogo et al., ApJS (2023) arXiv
Scientific publications
If you use NMMA for your research, please cite the following publications:
Main NMMA paper (required)
Backend — always cite: