★ Linear Oscillatory State-Space Models for Binary Neutron Star Detection ★

          @misc{ scivideos_oai:cds.cern.ch:3025628,
            doi = {},
            url = {https://videos.cern.ch/record/3025628},
            author = {},
            keywords = {},
            language = {en},
            title = {* Linear Oscillatory State-Space Models for Binary Neutron Star Detection *},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025628 see, \url{https://scivideos.org/cern-cds/3025628}}
          }
          

Abstract

The detection of long gravitational wave signals in noisy strain data demands models that can efficiently capture long-range temporal structure while remaining computationally tractable. In this talk we introduce Linear Oscillatory State-Space models (LinOSS), a class of sequence models rooted in linear dynamical systems theory, as an alternative to conventional deep learning architectures for gravitational wave data analysis. LinOSS processes time series through a learned latent state that evolves via structured recurrence, incorporating an oscillatory inductive bias that reflects the structure of the underlying physical systems. This enables the model to capture long-duration dependencies while scaling compute time logarithmically with sequence length. We then explore the application of this framework to the classification problem of distinguishing binary neutron star (BNS) merger signals from detector noise.

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