light-curve-preprocessing

To preprocess astronomical light curves for period analysis, you can apply outlier removal, trend flattening, and quality-flag handling to time-series photometry from missions like Kepler, TESS, and ground-based surveys using lightkurve.

Detrending light curves while preserving short-duration transits involves applying trend flattening to mitigate long-term instrumental and stellar variability, ensuring your period analysis retains important short-duration signals.

Yes, this light curve preprocessing handles data quality flags for TESS and Kepler missions, cleaning the time-series photometry by addressing data quality issues before you run period analysis.

You can optionally remove stellar variability from time-series photometry to focus your period searches, applying detrending and flattening techniques to isolate the specific signals you need for robust analysis.

Outlier removal matters when cleaning astronomical time-series data because it eliminates anomalous data points that can distort trend flattening and period analysis, ensuring your light curves yield robust and accurate results.

You should avoid detrending light curve data when the long-term stellar variability itself is the target of your analysis, as flattening removes these trends and may inadvertently distort the signals you want to study.