Speaker
Yu-Chiung Lin
(National Tsing Hua University)
Description
We investigated the relationship between the GW denoiser’s signal recovery and the prediction of the ML-based CBC analysis by training BBH, NSBH, and BNS binary classifiers on denoised strain data. We found that the GW detector can make confident detections when the signal recovery, measured by overlap, is larger than 0.2 for BBH, NSBH, and 0.1 for BNS. The results are consistent with our statements in the previous work. We also trained several regressors to investigate the relationship between the signal recovery and the parameter estimation results. Our work provides new insight into evaluating and developing future GW denoisers.
| Section | Cosmology |
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Primary author
Yu-Chiung Lin
(National Tsing Hua University)
