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

We introduce MANTRA, an annotated dataset of 4869 transient and 71207non-transient object lightcurves built from the Catalina Real Time TransientSurvey. We provide public access to this dataset as a plain text file tofacilitate standardized quantitative comparison of astronomical transient eventrecognition algorithms. Some of the classes included in the dataset are:supernovae, cataclysmic variables, active galactic nuclei, high proper motionstars, blazars and flares. As an example of the tasks that can be performed onthe dataset we experiment with multiple data pre-processing methods, featureselection techniques and popular machine learning algorithms (Support VectorMachines, Random Forests and Neural Networks). We assess quantitativeperformance in two classification tasks: binary (transient/non-transient) andeight-class classification. The best performing algorithm in both tasks is theRandom Forest Classifier. It achieves an F1-score of 96.25% in the binaryclassification and 52.79% in the eight-class classification. For theeight-class classification, non-transients ( 96.83% ) is the class with thehighest F1-score, while the lowest corresponds to high-proper-motion stars (16.79% ); for supernovae it achieves a value of 54.57% , close to the averageacross classes. The next release of MANTRA includes images and benchmarks withdeep learning models.

MANTRA: A Machine Learning reference lightcurve dataset for astronomical transient event recognition