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Python tools for GW AGN follow-up

Project description

🛰️ gw_agn_watcher

PyPI version License: MIT Build


Overview

gw_agn_watcher is a Python package for the automated crossmatching of gravitational-wave (GW) sky maps from the LIGO–Virgo–KAGRA (LVK) Collaboration with ** ZTF alerts and AGN catalogs** using ALeRCE infrastructure.
It enables systematic searches for electromagnetic counterparts to compact binary mergers, with a particular focus on mergers that may occur in active galactic nuclei (AGN) disks.


Key Features

  • Predicts the chirp mass of the GW superevent
  • 📡 Ingest LVK skymaps (.fits, HEALPix format)
  • 🌌 Crossmatch ZTF alerts with AGN catalogs (e.g., Milliquas)
  • 🧠 Apply ML-based filters using ALeRCE classifiers, Pan-STARRS morphology, and Deep Real/Bogus scores
  • 📅 Temporal and spatial filtering relative to the GW trigger time and sky localization
  • 🎯 Host-galaxy association and ranking based on 2σ GW distance posteriors
  • 🗺️ Visualization tools for probability maps, candidate locations, and sky coverage
  • 🔧 Modular and extensible — suitable for ToO planning, multi-messenger analyses, and survey follow-up

Installation

pip install gw_agn_watcher

Citation

If you use this package, please cite:

@ARTICLE{2026arXiv260304342B,
       author = {{Bommireddy}, Hemanth and {Forster}, Francisco and {McMahon}, Isaac and {Pavez Herrera}, Manuel and {Cartier}, Regis and {Olivares Estay}, Felipe and {Hern{\'a}ndez Garc{\'\i}a}, Lorena and {Mart{\'\i}nez Aldama}, Mary Loli and {Mu{\~n}oz Arancibia}, Alejandra},
        title = "{A Broker Integrated Algorithm for Gravitational Wave - Electromagnetic Counterpart Searches in O4a and O4b Runs}",
      journal = {arXiv e-prints},
     keywords = {High Energy Astrophysical Phenomena, Instrumentation and Methods for Astrophysics},
         year = 2026,
        month = mar,
          eid = {arXiv:2603.04342},
        pages = {arXiv:2603.04342},
          doi = {10.48550/arXiv.2603.04342},
archivePrefix = {arXiv},
       eprint = {2603.04342},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026arXiv260304342B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


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