From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning

CABRAL, J.B.; SÁNCHEZ, B.; RAMOS, F.; GUROVICH, S.; GRANITTO, P.M.; VANDERPLAS, J.

Astronomy and Computing

ELSEVIER SCIENCE BV

Año: 2018 vol. 25 p. 213 - 220

2213-1337

Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale publicsurvey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well asimproving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected dataas input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse forfeature 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 improvementcomprises a new Python package called feets, which is important for future code-refactoring for astronomical software tools.