Utilizing stellar mass estimates to identify gravitational wave host galaxies
ABSTRACT Stellar mass can enhance the ranking of potential hosts for compact binary coalescences identified by ground-based gravitational-wave detectors within large localization areas containing even thousands of galaxies. Despite its benefits, accurate stellar mass estimation is often time-consuming and computationally intensive. In this study, we implement four stellar mass estimation methods based on infrared magnitudes and compare them with values estimated with spectral energy distribution fitting from GAMA DR3, revealing strong correlations. We also introduce a method to calibrate the results from these estimation methods to match the reference values. Our analysis of simulated binary black hole events demonstrates that incorporating stellar mass improves the rank of actual hosts $\sim$80 per cent of cases. Furthermore, the improvement is comparable when stellar masses are derived from the tested estimation methods to when they are obtained directly from the simulated galaxy catalogue, demonstrating that simple stellar mass estimates can provide a computationally efficient alternative.
Authors
- M Pálfi (ORCID: https://orcid.org/0000-0001-5942-0470)
- G. Dálya (ORCID: https://orcid.org/0000-0003-3258-5763)
- P. Raffai
Institutions
- Eötvös Loránd University (HU)
- Centre National de la Recherche Scientifique (FR)
- Université Toulouse III - Paul Sabatier (FR)
- Université Fédérale de Toulouse Midi-Pyrénées (FR)
- Ghent University (BE)
- FZU ‒ Institute of Physics of the Academy of Sciences of the Czech Republic (CZ)
- Astronomy and Space (AU)
Publication Details
- Journal
- Monthly Notices of the Royal Astronomical Society
- Published
- 2025-04-04
- DOI
- https://doi.org/10.1093/mnras/staf537
- Citations
- 1
- Primary Topic
- Astronomical Observations and Instrumentation
- Type
- article
- Field-Weighted Citation Impact
- 1.33
Funders
- National Aeronautics and Space Administration
- California Institute of Technology
- Jet Propulsion Laboratory
- University of California, Los Angeles