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Searching for gravitational wave echo with group equivariant neural posterior estimation
Abstract
Current methods for the gravitational-wave (GW) echo searches generally require substantial computational time and resources. Recent advances in group equivariant neural posterior estimation (GNPE) have demonstrated the potential to substantially accelerate posterior inference. In this work, we adopt this framework and train a GNPE model to perform echo searches, significantly improving computational efficiency while maintaining comparable inference accuracy. We apply it to the O4a events and find no significant evidence for the GW echo.
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BibTeX
@misc{luo2026searching,
title = {{Searching for gravitational wave echo with group equivariant neural posterior estimation}},
author = {Jian-Wei Luo and Yun-Song Piao},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.18337},
eprint = {2609.18337},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.18337},
}
RIS
TY - UNPB
TI - Searching for gravitational wave echo with group equivariant neural posterior estimation
AU - Jian-Wei Luo
AU - Yun-Song Piao
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.18337
UR - https://arxiv.org/abs/2609.18337
AB - Current methods for the gravitational-wave (GW) echo searches generally require substantial computational time and resources. Recent advances in group equivariant neural posterior estimation (GNPE) have demonstrated the potential to substantially accelerate posterior inference. In this work, we adopt this framework and train a GNPE model to perform echo searches, significantly improving computational efficiency while maintaining comparable inference accuracy. We apply it to the O4a events and find no significant evidence for the GW echo.
ER -