Individual skill
conditioning
Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries data.
tasks/gravitational-wave-detection/environment/skills/conditioning/SKILL.md
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# Gravitational Wave Data Conditioning
Data conditioning is essential before matched filtering. Raw gravitational wave detector data contains low-frequency noise, instrumental artifacts, and needs proper sampling rates for computational efficiency.
## Overview
The conditioning pipeline typically involves:
1. High-pass filtering (remove low-frequency noise below ~15 Hz)
2. Resampling (downsample to appropriate sampling rate)
3. Crop filter wraparound (remove edge artifacts from filtering)
4. PSD estimation (calculate power spectral density for matched filtering)
## High-Pass Filtering
