English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning algorithms are highly useful for the classification of timeseries data in astronomy in this era of peta-scale public survey data releases.These methods can facilitate the discovery of new unknown events in mostastrophysical areas, as well as improving the analysis of samples of knownphenomena. Machine learning algorithms use features extracted from collecteddata as input predictive variables. A public tool called Feature Analysis forTime Series (FATS) has proved an excellent workhorse for feature extraction,particularly light curve classification for variable objects. In this study, wepresent a major improvement to FATS, which corrects inconvenient designchoices, minor details, and documentation for the re-engineering process. Thisimprovement comprises a new Python package called "feets", which is importantfor future code-refactoring for astronomical software tools.

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