Decoding the Effects of Varying Frequency Bands on GW Source Characterization

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Abstract:: Gravitational-wave (GW) science has opened new avenues for understanding
astrophysical phenomena, with precise signal characterization being essential
for interpreting these cosmic events. However, short-duration terrestrial noise
transients, known as â€œglitchesâ€, complicate this task. A common strategy for
mitigating the impact of glitches involves restricting the analysis to a reduced
frequency band. In my work, I utilize a neural posterior estimator, a deep
learning (DL) model, to investigate how varying frequency bands influence GW
parameter estimation for compact binary coalescences. I perform parameter
estimation on artificial data, which include both simulated waveforms and noise
realizations. By examining different frequency ranges, I aim to characterize the
typical effects of these restrictions on the inference of the source parameters.
Additionally, I focus on evaluating the use of a DL model in the GW parameter
estimation pipeline. By comparing results obtained with the DL model and the
conventional pipeline, I explore the effectiveness of deep learning approaches in
improving analysis speed and accuracy. My ongoing work involves refining the
networkâ€™s capabilities and analyzing the impact of varying frequency bands on
the reliability of parameter estimation.

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LIGO Document T2400274-v1