exoplanet-workflows

Selecting an exoplanet detection algorithm depends on your signal type and light curve data quality. A decision framework helps choose between tls, lomb-scargle, and bls to optimize detection sensitivity and minimize false positives.

Designing an exoplanet light curve pipeline requires structured steps for data loading, preprocessing, period-search, and validation. Best-practice workflows guide pipeline architecture to ensure repeatable analyses and reduce guesswork in exoplanet detection.

Handling data gaps and noise when searching for exoplanet transit signals requires specific troubleshooting techniques. The workflow provides guidance to address aliasing and data quality issues, improving period-search reliability across datasets.

This workflow supports parameter estimation in exoplanet detection. It integrates parameter estimation workflows with data quality considerations to help researchers validate light curve analyses and refine planetary transit measurements.

Use Lomb-Scargle over BLS for period search when analyzing specific signal types within light curves. The workflow's decision framework differentiates these methods based on signal characteristics and underlying data quality.