Astro-Multimessenger Modeling Software

• Astro-multimessenger modeling software is a collection of frameworks and toolkits designed to synthesize data from photons, neutrinos, cosmic rays, and gravitational waves.
• These platforms employ standardized plugins and APIs to abstract data integration, instrument responses, and joint likelihood construction using both frequentist and Bayesian methods.
• They enable comprehensive modeling of complex phenomena, such as binary neutron star mergers and blazar outbursts, by coupling modular physical solvers and automating reproducible workflows.

Astro-multimessenger modeling software encompasses frameworks, toolkits, and domain-specific solvers explicitly designed to synthesize and interpret datasets from multiple astrophysical messengers—encompassing photons (radio through gamma rays), neutrinos, cosmic rays, and gravitational waves—across heterogeneous instrument responses and physical regimes. These platforms enable joint analysis, self-consistent simulation, and parameter inference for complex astronomical sources by abstracting data integration, response folding, and likelihood construction. They constitute a foundational infrastructure for contemporary and next-generation observational programs targeting phenomena such as binary neutron star mergers, blazar outbursts, and supernovae, where high-fidelity multimessenger modeling is critical.

1. Unifying Statistical Inference and Plugin-Based Architectures

A central paradigm in multimessenger modeling is the unification of joint likelihood analysis across disparate datasets, each with distinct data structures, detector responses, and statistical frameworks. The Multi-Mission Maximum Likelihood (3ML) framework exemplifies this approach (Vianello et al., 2015). In 3ML, the core statistical formalism constructs the total likelihood as a product over instrument-specific likelihoods: $L(\theta) = \prod_n P(D_n | M(\theta))$ where each $D_n$ is a dataset (e.g., Fermi-LAT, HAWC, IceCube events) and $M(\theta)$ is the astrophysical source model parameterized by $\theta$. Expected event rates are modeled by forward-folding the source SED, $S(E; \theta)$, through each instrument's response, $R_i(E_\mathrm{obs}|E)$, with $P(D_n|M)$ typically formulated as Poissonian or unbinned likelihoods depending on data type.

Instrument- and messenger-specific plugins architecturally encapsulate all data I/O, background modeling, and response convolution, permitting analysis that is agnostic to mission-specific software and data structures. New messenger classes are supported through plugin development that exposes standardized log-likelihood evaluation and optional nuisance parameter interfaces.

The 3ML workflow supports both frequentist (Minuit/MIGRAD-based maximum likelihood) and Bayesian (MCMC via emcee, nested sampling via MultiNest) inference engines, with transparent switching via its Fit API. Empirical use cases range from broadband SED fits to fully joint gamma-ray/TeV/neutrino source characterizations and are extensible to new data classes such as gravitational waves, cosmic rays, and VLBI visibilities via the plugin model (Vianello et al., 2015, Fan et al., 2021).

2. Modular Domain-Specific Physical Solvers and Code Coupling

Comprehensive modeling of multimessenger phenomena requires coupling solvers across distinct physics domains (e.g., N-body stellar dynamics, relativistic hydrodynamics, nuclear equation-of-state modeling, radiative transfer), each of which may be best addressed by legacy or community codes. The Astrophysics Multipurpose Software Environment (AMUSE) implements a hierarchical, modular framework driven by a Python frontend, in which domain codes (Fortran, C, C++, or Python; serial or MPI) are executed as isolated worker processes (Elteren et al., 2014, McMillan et al., 2011).

Communication between solvers occurs via channels that map particle or grid attributes peer-to-peer, while stopping conditions and services permit dynamic hand-off of state for specialized event-handling (e.g., close encounters, supernovae). Operator splitting methods (e.g., the Bridge integrator) achieve time-synchronized evolution across domains with differing physics, allowing for symplectic integration when coupling gravitational, hydrodynamics, and radiative processes. Conservation at fluid–gravity interfaces is enforced explicitly via exchange of field quantities in weak form, maintaining physical fidelity.

Supported physics domains include:

• Gravitational Dynamics: N-body and tree codes (e.g., ph4, Huayno), relevant for gravitational-wave precursor modeling.
• Hydrodynamics: SPH (Fi, Gadget2) and grid-based (Athena, MPI-AMRVAC) solvers for gas evolution and EM emission.
• Radiative Transfer: Moment-based and Monte Carlo schemes (SimpleX, SPHRay).
• Stellar Evolution: Both analytic (SSE, SeBa) and tabulated EOS models.
• Neutrino Transport: Under active development, prototype interfaces to NuLib.

Extending AMUSE to new messengers—such as gravitational wave emission or neutrino transport—involves implementing the standard interface (evolve_model, get_state) in a module that can communicate via MPI, permitting plug-and-play integration (Elteren et al., 2014).

3. Specialized Multimessenger Analysis Frameworks

For scenarios with tightly coupled photon/neutrino/GW observables, several software environments provide self-consistent, domain-specific modeling tools:

• NMMA (Nuclear-physics and Multi-Messenger Astrophysics): Designed for binary neutron star mergers, NMMA integrates GW waveform modeling (LALSuite), nuclear EOS construction (chiral EFT, speed-of-sound extension), kilonova radiative transfer (Monte Carlo, GPR/NN surrogates), GRB afterglow simulation, and observational constraints (NICER, pulsar masses) into a unified joint-likelihood Bayesian inference engine (dynesty via bilby, parallel MPI) (Pang et al., 2022). Physical models are tightly coupled, empirical likelihoods are evaluated for GW, kilonova, and GRB afterglow, and extensibility permits rapid incorporation of new physics modules.
• LeHaMoC and AM³: These codes solve coupled time- and energy-dependent kinetic equations for electrons, positrons, protons, photons, and neutrinos (as well as π, μ in AM³), with complete inclusion of synchrotron, IC, Bethe–Heitler, and hadronic processes in time-dependent, homogeneous zones. Both implement energy-implicit or hybrid solvers with detailed cascades, allowing for rapid steady-state solution and modeling of temporal variability and secondary production. Outputs (SEDs, neutrino spectra, time series) can be readily interfaced with detector effective areas and used in multi-messenger hypothesis testing (Stathopoulos et al., 2023, Klinger et al., 2023).
• Heavy Cosmic-Ray Propagation Codes: For modeling the injection, propagation, and secondary production of arbitrary nuclear species (up to iron), heavy-nuclei transport software tracks all relevant processes (photo-meson, photo-disintegration, synchrotron, IC, decay) and feeds back x-ray/gamma-ray secondaries into the photon field, enabling nonlinear, composition-dependent predictions of multimessenger observables in arbitrary, evolving radiation fields (Merten et al., 2023).

4. Multimessenger Data Integration and Workflow Automation

Astro-multimessenger workflows must accommodate a variety of data ingestion formats (ASCII, HDF5, FITS for light curves; public LIGO strain; radio/X-ray/γ-ray and neutrino event lists) and process them through instrument-specific or universal software stacks. Automated data interfaces and configuration-driven orchestration are standard: e.g., YAML or JSON config files specify simulation or inference parameters, physics module selection, and messenger inclusion.

Modern frameworks (e.g., NMMA, LeHaMoC, AM³, 3ML with i3mla or custom plugins) support fully scripted, parallel, and reproducible workflows. They typically include Python APIs and CLIs for setup, execution, and output, as well as plotting and post-processing utilities for SED, time-series, and posterior visualization (Vianello et al., 2015, Stathopoulos et al., 2023, Klinger et al., 2023, Pang et al., 2022).

Performance scaling is achieved through multi-core, MPI-parallel execution, cache optimization (precomputed response grids or matrix rates), and smart domain decomposition. In 3ML, each plugin only recomputes likelihoods when relevant parameters change, maximally exploiting conditional independence and cache locality (Vianello et al., 2015).

5. Extension Mechanisms and Prospects for Future Development

The extensibility of astro-multimessenger modeling software is enabled by modular plugin/API contracts, process- or domain-driven abstraction, and flexible mathematical backends. Key extension routes include:

• New Messenger Implementations: To accommodate additional messengers (e.g., neutrinos, gravitational waves, cosmic rays), a new plugin or module must provide forward-folding of source models through detector response (as captured by an effective area, PSF, or more general response tensor) and expose likelihood evaluation methods. In AMUSE and 3ML, this is a standardized, minimal intervention (Vianello et al., 2015, Elteren et al., 2014).
• Custom Physics Modules: In AM³, LeHaMoC, and heavy-nuclei codes, new interactions, source models, or target fields are incorporated by subclassing process classes, writing analytic or tabulated cross-section/yield definitions, and linking these in configuration files—the core solver adapts automatically (Klinger et al., 2023, Stathopoulos et al., 2023, Merten et al., 2023).
• Performance Tuning and Scalability: For high-dimensional parameter inference or large event datasets, current tools support parallel sampling (MPI, multithreading), caching of repeat computations, and optional GPU or compiled acceleration.

As the scope of multimessenger astrophysics expands, with increasing event rates, improved cross-calibration for multi-instrument datasets, and emerging messenger classes, a plausible implication is that software abstraction layers, standardized APIs, and semantically rich configuration will become even more central. The architecture and plugin designs of 3ML, AMUSE, NMMA, LeHaMoC, AM³, and related toolkits provide robust mechanisms for the assimilation of next-generation observational inputs and theoretical modules across the entire domain of multimessenger high-energy astrophysics (Vianello et al., 2015, Elteren et al., 2014, Pang et al., 2022, Klinger et al., 2023, Stathopoulos et al., 2023, Merten et al., 2023, Fan et al., 2021).