★ From Inspiral to Inference: BNS Parameter Estimation with State Space Models ★

          @misc{ scivideos_oai:cds.cern.ch:3025625,
            doi = {},
            url = {https://videos.cern.ch/record/3025625},
            author = {},
            keywords = {},
            language = {en},
            title = {* From Inspiral to Inference: BNS Parameter Estimation with State Space Models *},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025625 see, \url{https://scivideos.org/cern-cds/3025625}}
          }
          

Abstract

Fast and accurate parameter estimation of binary neutron star (BNS) mergers, gravitational wave events with electromagnetic counterparts, remains a central challenge in multimessenger astronomy. Building on the State Space Model (SSM) framework presented in the companion talk, we directly regress BNS merger source parameters from raw gravitational wave time series, without sampling-based inference. As a first demonstration, we focus on the chirp mass, the dominant parameter governing the inspiral waveform. We show that regression succeeds not only from windows centered on the merger, but also from pre-merger inspiral segments, with implications for early-warning detection pipelines. By placing a Gaussian prior on the regression target, we additionally regress uncertainty estimates on the inferred chirp mass. We further demonstrate the physical meaningfulness of these uncertainties by showing they discriminate between signal and background events with performance comparable to existing pipelines. We discuss prospects for extending this framework to additional source parameters, including sky localization, and explore potential improvements through preprocessing strategies such as denoising.

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