Multimessenger App - Analysis Module

Overview

This repository is part of a Multimessenger App designed to analyze data from different astronomical sources. In multimessenger astronomy, signals from various messengers—such as gravitational waves, radio waves, and electromagnetic waves—are combined to gain a more comprehensive understanding of astrophysical phenomena.

In multi-messenger astronomy, signals from different astrophysical messengers (e.g., gravitational waves and radio waves) provide complementary insights into the same cosmic events. This repository enables joint inference by:

• Performing parameter estimation from gravitational wave data using Dingo-BNS, a machine learning framework that provides fast, accurate inference for binary neutron star (BNS) mergers.

• Performing light curve fitting using afterglowpy to estimate viewing angle and luminosity distance from off-axis jet observations.

• Conducting overlap analysis by comparing posterior distributions (e.g., $\theta_{\rm obs}$ and $d_L$) from both modalities, helping to reduce degeneracies and improve cosmological measurements, such as the Hubble constant ($H_0$).

Repository Structure

MMA_MultimessengerAnalysis/
├── ParameterEstimation/    # Inference using Dingo-BNS on gravitational wave data
├── OverlapAnalysis/        # Scripts for overlapping GW and EM posteriors (e.g., DL and theta_obs)
└── README.md

MMA Module Purpose

This module streamlines joint GW–EM inference by integrating outputs from gravitational wave parameter estimation and radio afterglow modeling. A key goal is to illustrate how joint constraints can improve the measurement of cosmological parameters like the Hubble constant ($H_0$). Specifically:

• Dingo-BNS provides posteriors on $d_L$ and $\theta_{\rm view}$ from GW observations.
• Afterglowpy-based radio fitting reduces parameter degeneracy by constraining jet orientation and energy.
• The overlap analysis aligns posteriors from both sources to quantify combined inference capabilities.

Getting Started

Each subfolder (e.g., ParameterEstimation/, OverlapAnalysis/) includes example scripts and configuration files to reproduce the analyses.

To run a full end-to-end joint analysis:

1. Run GW parameter estimation using ParameterEstimation/dingo_inference.py.
2. Fit the radio afterglow using afterglowpy scripts from your corresponding Radio module.
3. Perform joint comparison in OverlapAnalysis/ using corner plots or KDE-based contours.

Related Projects

This repo focuses on performing joint analysis between gravitational and radio wave data. For gravitational wave and radio wave analysis, please visit Gravitational Wave Analysis Repo and Radio Wave Analysis Repo. Together, these repositories work within the multimessenger framework to capture and analyze various cosmic events.