{"id":2340,"date":"2026-08-16T17:16:56","date_gmt":"2026-08-16T21:16:56","guid":{"rendered":"https:\/\/web.uri.edu\/gravity\/uri-gravity-group-researchers-advance-gravitational-wave-modeling-detection-and-data-analysis\/"},"modified":"2026-08-16T17:16:56","modified_gmt":"2026-08-16T21:16:56","slug":"uri-gravity-group-researchers-advance-gravitational-wave-modeling-detection-and-data-analysis","status":"publish","type":"post","link":"https:\/\/web.uri.edu\/gravity\/uri-gravity-group-researchers-advance-gravitational-wave-modeling-detection-and-data-analysis\/","title":{"rendered":"URI Gravity Group Researchers Advance Gravitational-Wave Modeling, Detection, and Data Analysis"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Researchers connected with URI\u2019s gravity group have contributed to a set of recent short-author papers that push forward several key parts of modern gravitational-wave science: modeling the final products of black hole mergers, accelerating waveform calculations with machine learning, and improving the way gravitational-wave detectors identify and interpret signals.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">One recent paper, <a href=\"https:\/\/arxiv.org\/abs\/2608.00934\"><strong>\u201cUnified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers\u201d<\/strong><\/a>, by Tousif Islam, Digvijay Wadekar, and Gaurav Khanna, presents new analytic models for predicting the final mass, spin, luminosity, and recoil of merged black holes. The work combines thousands of numerical-relativity simulations with black-hole-perturbation-theory calculations, extending accuracy across regimes from comparable-mass binaries to extreme mass ratios. These models are intended for applications in gravitational-wave astronomy, astrophysical population studies, and cosmology.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A second paper, <a href=\"https:\/\/arxiv.org\/abs\/2607.24960\"><strong>\u201cFast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms\u201d<\/strong><\/a>, by Michael P\u00fcrrer, Ashwin Girish, Lucy M. Thomas, Scott E. Field, and Vijay Varma, develops a neural-network surrogate for precessing binary black hole waveforms. The model reproduces the NRSur7dq4 waveform family with high accuracy while running much faster on GPUs. Because the full waveform-to-likelihood pipeline is implemented in JAX and is differentiable, it opens the door to faster parameter estimation and gradient-based inference methods for gravitational-wave data analysis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The group\u2019s detector-analysis work is represented by <a href=\"https:\/\/arxiv.org\/abs\/2608.09804\"><strong>\u201cSGNAX: a unified matched-filter and excess-power pipeline for gravitational-wave detector characterization\u201d<\/strong><\/a>, by Zach Yarbrough, Olivia Godwin, Derek Davis, and Gabriela Gonz\u00e1lez. SGNAX is an open-source pipeline that unifies two important approaches to detector characterization in a single streaming Python framework. It can process a full day of gravitational-wave strain data in minutes, supports both CPU and GPU execution, and is designed for offline and low-latency detector studies.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Together, these papers highlight the range of computational work behind gravitational-wave discovery. Accurate black hole remnant models help scientists interpret the astrophysical meaning of observed mergers. Fast waveform surrogates make large-scale inference more practical. Detector-characterization pipelines help separate real astrophysical signals from instrumental artifacts. Across theory, computation, and data analysis, URI-affiliated researchers continue to contribute tools that support the next generation of gravitational-wave astronomy.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent short-author papers from URI-affiliated researchers advance black hole merger modeling, fast waveform generation, and gravitational-wave detector characterization.<\/p>\n","protected":false},"author":4052,"featured_media":1820,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[2,5],"tags":[],"class_list":["post-2340","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-publications"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/posts\/2340","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/users\/4052"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/comments?post=2340"}],"version-history":[{"count":0,"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/posts\/2340\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/media\/1820"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/media?parent=2340"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/categories?post=2340"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.uri.edu\/gravity\/wp-json\/wp\/v2\/tags?post=2340"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}