matched-filtering

Matched filtering is a technique for detecting gravitational-wave signals in noisy detector data by correlating known template waveforms with the data to identify high-SNR events. It compares incoming strain data against theoretical waveform models to find hidden signals.

To calculate SNR time series for gravitational waves using PyCBC, supply conditioned strain data, a power spectral density (PSD) estimate, and template parameters to generate and evaluate the matched-filter output. This process locates candidate events by finding peaks in the resulting SNR time series.

Matched filtering works with frequency-domain waveform approximants to enable fast, efficient filtering. You must select appropriate waveform approximants and ensure templates are resized to match data or PSD lengths for compatible frequency-domain workflows.

Cropping edge artifacts after matched filtering is required because the filtering process introduces boundary effects at the start and end of the data segment. Removing these artifacts prevents false-positive peak detections when locating candidate gravitational-wave events.

Data conditioning before a matched-filter search requires matching sampling rates and resizing templates to data or PSD lengths. You must also estimate the PSD to accurately characterize detector noise before generating the SNR time series for peak finding.