Year: 2026
Type: article
Abstract: Gravitational waves (GWs) are unique messengers as they travel through the Universe without alteration except for gravitational lensing. Their long wavelengths make them susceptible to diffraction by cosmic structures, providing an unprecedented opportunity to map dark matter substructures. Identifying lensed events requires the analysis of thousands to millions of simulated events to reach high statistical significances. This is computationally prohibitive with standard GW parameter estimation methods. We exploit DINGO-lensing, a deep-learning algorithm that accelerates the inference from CPU days to minutes to thoroughly reanalyze GW231123, the most promising lensing candidate to date. By performing more than 200,000 simulations with 3 different waveform models, we find that its statistical significance is below 4$σ$ and the event cannot be claimed as lensed. We observe that 8% of GW231123-like nonlensed simulations favor lensing, which could be explained by the self-similarity of short-duration signals. Still, 58% of GW231123-like lensed simulations have larger support for lensing, showing that higher detection statistics are possible. We show that analyzing simulations with different waveform models only lowers the significance, highlighting the relevance of waveform systematics. Although GW231123 exposes the challenges of claiming the first GW lensing detection, our deep-learning methods have demonstrated to be powerful enough to enable the upcoming discovery of lensed GWs.
DOI: 10.1103/5292-948q
Authors: Juno C. L. Chan, Jose María Ezquiaga, Rico K. L. Lo, Joey Bowman, Lorena Magaña Zertuche, Luka Vujeva
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science and Technology Council, National Science Foundation, Villum Fonden, European Commission, National Research Foundation, Ministry of Education, Culture, Sports, Science and Technology, Danmarks Grundforskningsfond, Københavns Universitet, Academia Sinica, National Research Foundation of Korea, Centre National de la Recherche Scientifique, Istituto Nazionale di Fisica Nucleare, Ministry of Science and ICT, South Korea, National Science and Technology Council, Max Planck School of Photonics, Horizon 2020 Framework Programme, Science and Technology Facilities Council, Australian Research Council, Japan Society for the Promotion of Science, Instituto Nazionale di Fisica Nucleare, HORIZON EUROPE Marie Sklodowska-Curie Actions
OpenAlex ID: W4417531503