A technical toolkit for cleaning astronomical time-series data to prepare for exoplanet detection and period analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install exoplanet-detection-period-light-curve-preprocessing
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 exoplanet-detection-period-light-curve-preprocessing 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 Light Curve Preprocessing?
Light Curve Preprocessing is a specialized utility within the Openclaw Skills ecosystem designed for astronomers and data scientists. It provides standardized techniques for cleaning raw astronomical light curves, which are often plagued by instrumental noise, cosmic rays, and long-term stellar variability. By leveraging these Openclaw Skills, researchers can effectively isolate periodic signals like exoplanet transits from background noise.
The skill focuses on the critical balance between noise reduction and signal preservation. It utilizes robust libraries like lightkurve and numpy to implement sophisticated filtering, such as Savitzky-Golay detrending and iterative sine fitting, ensuring that the data is primed for high-accuracy periodograms and discovery pipelines.
Light Curve Preprocessing Use Cases
- Preparing TESS or Kepler data for exoplanet transit searches.
- Removing instrumental drift and systematic noise from astronomical time series.
- Cleaning data sets by filtering quality flags and removing cosmic ray outliers.
- Detrending long-term stellar variability to isolate short-duration periodic events.
- Pre-processing stellar light curves before performing Fourier analysis or BLS searches.
How Light Curve Preprocessing Works
- The process begins with quality filtering, where specific data flags are used to remove unreliable cadences.
- Sigma clipping is applied to the remaining data to identify and remove statistical outliers like flares.
- Trend removal is performed using flattening algorithms to eliminate low-frequency variations while preserving the shape of short-duration dips.
- Optional high-frequency filtering is applied via iterative sine fitting for complex stellar signatures.
- The clean, detrended light curve is output alongside flux errors, ready for period analysis.
Light Curve Preprocessing Setup
To use this light curve preprocessing functionality, ensure you have the necessary astronomical data processing libraries installed:
pip install lightkurve numpy matplotlib
Light Curve Preprocessing Data Schema & Taxonomy
The skill processes and generates time-series data structures. The primary data requirements and outputs include:
| Attribute | Description | Format |
|---|---|---|
| Time | The temporal coordinate of observations | array/float64 |
| Flux | The observed brightness of the celestial body | array/float64 |
| Flux Error | Measurement uncertainty for weighted analysis | array/float64 |
| Quality Flags | Bitmask integers representing data reliability | int |
| Clean Mask | A boolean array used to track removed data points | bool |
Light Curve Preprocessing Advanced Features
- Iterative Sine Fitting: Advanced removal of dominant periodic signals for multi-component stellar analysis.
- Savitzky-Golay Flattening: Dynamic window length selection to preserve transit morphology.
- Multi-Pass Outlier Detection: Sequential cleaning before and after detrending for maximum data purity.
- Custom Flag Mapping: Flexible handling of varying data quality conventions across different telescope missions.
SKILL.md
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