Skill: wu-uk/gravitational-wave-detection-conditioning (version 0.1.0)

--- name: conditioning description: 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. --- # 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 Remove low-frequency noise and instrumental artifacts: ```python from pycbc.filter import highpass # High-pass filter at 15 Hz (typical for LIGO/Virgo data) strain_filtered = highpass(strain, 15.0) # Common cutoff frequencies: # 15 Hz: Standard for ground-based detectors # 20 Hz: Higher cutoff, more aggressive noise removal # 10 Hz: Lower cutoff, preserves more low-frequency content ``` **Why 15 Hz?** Ground-based detectors like LIGO/Virgo have significant low-frequency noise. High-pass filtering removes this noise while preserving the gravitational wave signal (typically >20 Hz for binary mergers). ## Resampling Downsample the data to reduce computational cost: ```python from pycbc.filter import resample_to_delta_t # Resample to 2048 Hz (common for matched filtering) delta_t = 1.0 / 2048 strain_resampled = resample_to_delta_t(strain_filtered, delta_t) # Or to 4096 Hz for higher resolution delta_t = 1.0 / 4096 strain_resampled = resample_to_delta_t(strain_filtered, delta_t) # Common sampling rates: # 2048 Hz: Standard, computationally efficient # 4096 Hz: Higher resolution, better for high-mass systems ``` **Note**: Resampling should happen AFTER high-pass filtering to avoid aliasing. The Nyquist frequency (half the sampling rate) must be above the signal frequency of interest. ## Crop Filter Wraparound Remove edge artifacts introduced by filtering: ```python # Crop 2 seconds from both ends to remove filter wraparound conditioned = strain_resampled.crop(2, 2) # The crop() method removes time from start and end: # crop(start_seconds, end_seconds) # Common values: 2-4 seconds on each end # Verify the duration print(f"Original duration: {strain_resampled.duration} s") print(f"Cropped duration: {conditioned.duration} s") ``` **Why crop?** Digital filters introduce artifacts at the edges of the time series. These artifacts can cause false triggers in matched filtering. ## Power Spectral Density (PSD) Estimation Calculate the PSD needed for matched filtering: ```python from pycbc.psd import interpolate, inverse_spectrum_truncation # Estimate PSD using Welch's method # seg_len: segment length in seconds (typically 4 seconds) psd = conditioned.psd(4) # Interpolate PSD to match data frequency resolution psd = interpolate(psd, conditioned.delta_f) # Inverse spectrum truncation for numerical stability # This limits the effective filter length psd = inverse_spectrum_truncation( psd, int(4 * conditioned.sample_rate), low_frequency_cutoff=15 ) # Check PSD properties print(f"PSD length: {len(psd)}") print(f"PSD delta_f: {psd.delta_f}") print(f"PSD frequency range: {psd.sample_frequencies[0]:.2f} - {psd.sample_frequencies[-1]:.2f} Hz") ``` ### PSD Parameters Explained - **Segment length (4 seconds)**: Longer segments give better frequency resolution but fewer averages. 4 seconds is a good balance. - **Low frequency cutoff (15 Hz)**: Should match your high-pass filter cutoff. Frequencies below this are not well-characterized. ## Best Practices 1. **Always high-pass filter first**: Remove low-frequency noise before resampling 2. **Choose appropriate sampling rate**: 2048 Hz is standard, 4096 Hz for high-mass systems 3. **Crop enough time**: 2 seconds is minimum, but may need more for longer templates 4. **Match PSD cutoff to filter**: PSD low-frequency cutoff should match high-pass filter frequency 5. **Verify data quality**: Plot the conditioned strain to check for issues ## Dependencies ```bash pip install pycbc ``` ## References - [PyCBC Tutorial: Waveform & Matched Filter](https://colab.research.google.com/github/gwastro/pycbc-tutorials/blob/master/tutorial/3_WaveformMatchedFilter.ipynb) - Practical tutorial covering data conditioning, PSD estimation, and matched filtering - [PyCBC Filter Documentation](https://pycbc.org/pycbc/latest/html/pycbc.filter.html) - [PyCBC PSD Documentation](https://pycbc.org/pycbc/latest/html/pycbc.psd.html) ## Common Issues **Problem**: PSD estimation fails with "must contain at least one sample" error - **Solution**: Ensure data is long enough after cropping (need several segments for Welch method) **Problem**: Filter wraparound artifacts in matched filtering - **Solution**: Increase crop amount or check that filtering happened before cropping **Problem**: Poor SNR due to low-frequency noise - **Solution**: Increase high-pass filter cutoff frequency or check PSD inverse spectrum truncation


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