SIAM Undergraduate Research Online

Volume 19

In This Volume

  • DOI: 10.1137/25S1766772

    Authors

    Casey Holman (Corresponding author – Lawrence University)

    Project Advisors

    Casey Holman (Corresponding author – Lawrence University)

    Abstract

    Gravitational Wave (GW) data is contaminated by many instrumental and environmental artifacts which adversely affect the search sensitivity for astrophysical signals. For the artifacts that cannot be eliminated in hardware, data processing must instead be used to identify and filter them out. Once such artifact is the Wandering Line (WL), a chirp with unpredictable frequency evolution that varies smoothly across a wide frequency band over a sustained period. Due to their large and unpredictable frequency range, WLs are difficult to filter using traditional line removal techniques without risking the removal of astrophysical signals. We propose a novel approach in which the WL is split into smaller segments and Particle Swarm Optimization (PSO) is used to fit a quadratic chirp model that is then subtracted from the data. We test this methodology on simulated WLs generated using random cubic spline frequency functions and added i.i.d. Gaussian white noise. Our results demonstrate that this approach successfully filters simulated WL artifacts with noise that has a standard deviation (s.d.) up to 5 times the WL amplitude. This is more than the noise s.d. to amplitude ratio of 4 we estimate from LIGO data, showing promise for suppressing such artifacts in real GW observations.

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