From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
This is an incremental improvement for astronomers using machine learning to classify variable objects from large-scale survey data.
The authors tackled the limitations of the FATS feature extraction tool for astronomical time series by introducing 'feets', a re-engineered Python package that corrects design flaws and improves documentation.
Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called "feets", which is important for future code-refactoring for astronomical software tools.