conditioning

Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-p...

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The skill is an instruction-only guide for PyCBC-based gravitational-wave data conditioning and its requirements and instructions are consistent with that purpose.

Purpose & Capability

Name/description, the step-by-step pipeline (high-pass, resample, crop, PSD), and the single declared dependency (pycbc) are all appropriate and proportional to a data-conditioning guide for gravitational-wave strain data.

Instruction Scope

SKILL.md stays within scope: it shows PyCBC calls and parameters for filtering, resampling, cropping, and PSD estimation. It does not instruct reading unrelated files, accessing credentials, or sending data to external endpoints beyond recommending installing pycbc and linking public docs.

Install Mechanism

No install spec is present; the file simply recommends 'pip install pycbc'. As an instruction-only skill this is low-risk. The recommendation references a normal public Python package rather than arbitrary downloads.

Credentials

The skill requests no environment variables, credentials, or config paths. That is proportional for an offline data-conditioning recipe.

Persistence & Privilege

always is false and the skill is user-invocable. It does not request persistent/system-wide changes or elevated privileges.

Assessment

This is a documentation-style skill that provides PyCBC code snippets and best practices for conditioning gravitational-wave strain data. Before using it: run the examples in an isolated Python virtual environment, install pycbc from official sources (PyPI or project documentation), and ensure system build dependencies for pycbc are met (pycbc can pull compiled libs). Validate the recommended cutoff, resampling rates, and crop lengths on your own data — they are typical defaults but may need tuning. Because the skill is instruction-only, it itself does not install code or access credentials; risks come from the external packages you choose to install and from applying the processing to sensitive data, so review those separately.

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:

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:

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:

# 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:

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

pip install pycbc

References

• PyCBC Tutorial: Waveform & Matched Filter - Practical tutorial covering data conditioning, PSD estimation, and matched filtering
• PyCBC Filter Documentation
• PyCBC PSD Documentation

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