Fast Simulation of Gravitational-Wave Signals and Glitch Populations for Data Analysis

          @misc{ scivideos_oai:cds.cern.ch:3025599,
            doi = {},
            url = {https://videos.cern.ch/record/3025599},
            author = {},
            keywords = {},
            language = {en},
            title = {Fast Simulation of Gravitational-Wave Signals and Glitch Populations for Data Analysis},
            publisher = {},
            year = {2026},
            month = {may},
            note = {oai:cds.cern.ch:3025599 see, \url{https://scivideos.org/cern-cds/3025599}}
          }
          

Abstract

Accurate signal models and the disruptive presence of transient noise artifacts (glitches) impose key limitations on the sensitivity of gravitational-wave detectors. Efficient simulation of both is essential for data analysis. Fast generation of gravitational-wave signals is required for detection and parameter estimation, where waveforms are evaluated repeatedly within inference pipelines. At the same time, realistic modeling of glitches is needed to represent unknown noise populations and to stress test search and inference methods. This talk compares state-of-the-art approaches for fast simulation of both gravitational-wave signals and glitches. We focus on generative deep learning methods that learn data distributions and enable rapid generation in the time domain. These methods speed up simulations compared to traditional approaches while preserving key features of signals and noise, enabling more efficient and realistic data analysis.

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