A sophisticated implementation of matched filtering techniques for extracting gravitational wave signals from detector noise using PyCBC templates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install gravitational-wave-detection-matched-filtering
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install gravitational-wave-detection-matched-filtering using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Matched Filtering for Gravitational Wave Detection?
This Openclaw Skills module enables researchers and developers to perform matched filtering, the primary technique for detecting gravitational wave signals within noisy detector data. By correlating known template waveforms with preprocessed strain data, the skill identifies signals with a high signal-to-noise ratio (SNR), facilitating the discovery of binary black hole mergers and other cosmic events.
Integrating seamlessly into the scientific ecosystem, this skill provides a streamlined approach to handling complex astrophysical data. Whether you are working with time-domain or frequency-domain waveforms, this tool leverages Openclaw Skills to simplify the lifecycle of signal detection from waveform generation to peak finding and data cropping.
Matched Filtering for Gravitational Wave Detection Use Cases
- Detecting binary black hole mergers in LIGO or Virgo detector strain data.
- Generating high-fidelity theoretical waveforms for astrophysical research.
- Performing SNR analysis across different detector networks using varied approximants.
- Automating the identification of gravitational wave triggers in massive datasets using Openclaw Skills workflows.
How Matched Filtering for Gravitational Wave Detection Works
- Generate a template waveform based on physical parameters like solar mass and frequency cutoffs using get_td_waveform or get_fd_waveform.
- Prepare the detector data by applying high-pass filters and calculating the noise Power Spectral Density (PSD).
- Resize and align the template to match the data length or frequency resolution for proper correlation.
- Apply the matched filter algorithm to cross-correlate the template with the conditioned detector data.
- Post-process the output by cropping edge artifacts and locating the peak magnitude of the complex SNR series.
Matched Filtering for Gravitational Wave Detection Setup
To utilize this skill, you must install the PyCBC and NumPy libraries. Use the following command in your terminal:
pip install pycbc numpy
Once installed, you can import the filtering and waveform modules to begin processing gravitational wave data within your Openclaw Skills environment.
Matched Filtering for Gravitational Wave Detection Data Schema & Taxonomy
The skill organizes its signal processing pipeline using the following data structures:
| Data Component | Description | Type |
|---|---|---|
| Template | The theoretical waveform (hp or hc) generated by an approximant | TimeSeries or FrequencySeries |
| Conditioned Data | High-passed and whitened strain data from detectors | TimeSeries |
| PSD | Power Spectral Density representing the detector noise profile | FrequencySeries |
| SNR Series | The output of the matched filter showing correlation over time | Complex TimeSeries |
| Peak SNR | The maximum magnitude found within the SNR Series | Float |
Matched Filtering for Gravitational Wave Detection Advanced Features
- Support for both Time-Domain and Frequency-Domain waveform generation to optimize computational efficiency.
- Cyclic time shifting capabilities for precise alignment of merger events at the start of templates.
- Extensive library of physical approximants including IMRPhenomD and SEOBNRv4_opt for diverse system modeling.
- Automated edge cropping logic to remove artifacts corrupted by PSD filtering and template length within Openclaw Skills.
- Flexible low-frequency cutoff handling to match high-pass filter configurations.
SKILL.md
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