light-curve-preprocessing

To preprocess light curves for period analysis, apply sigma clipping to remove outliers, flatten the data to eliminate long-term trends, and filter out poor quality flags. This prepares time-series data for robust periodogram analysis and transit searches.

Remove outliers from TESS or Kepler time-series data using sigma clipping. This process identifies and discards data points that deviate significantly from the statistical norm, preventing anomalous signals from distorting intrinsic stellar variability.

Lightkurve is supported as the underlying framework for this light curve preprocessing pipeline. It enables handling data quality flags, flattening long-term trends, and stabilizing time-series data from missions like Kepler and TESS.

The best way to detrend astronomical light curves for transit searches is to apply a flattening filter that removes low-frequency variability while preserving high-frequency planetary transit signals. Visual verification of each step ensures signal preservation.

Data quality flag handling matters for exoplanet light curves because flagged points often represent instrumental systematics or cosmic ray events. Filtering these ensures that reproducible preprocessing steps do not interpret artifacts as intrinsic stellar variability.