Speaker
Description
Estimating the background noise power is essential for gravitational wave (GW) analysis. Typically, people compute the power spectral density (PSD) of the noise background using the Welch method, which is a sliding average over a long period of data, usually spanning tens to thousands of seconds. However, the detector strain data is non-stationary and sometimes glitchy due to the detector's bad status. This can make the estimated noise background not representative of the data we are analyzing. In such cases, people can either manually select a relatively clean segment or use the Monte Carlo method to estimate the noise PSD. Still, the former requires additional manpower for the search, and the latter requires Monte Carlo sampling and is computationally expensive. In this work, we developed a machine-learning method to estimate the PSD from the data we analyzed, without accounting for prior data quality.
| Participate the oral/poster presentation award competition | No |
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