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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Our understanding of the Universe has profited from deliberate, targetedstudies of known phenomena, as well as from serendipitous, unexpecteddiscoveries, such as the discovery of a complex variability pattern in thedirection of KIC 8462852 (Boyajian's star). Upcoming surveys, such as the VeraC. Rubin Observatory Legacy Survey of Space and Time (LSST), will explore theparameter space of astrophysical transients at all time scales, and offer theopportunity to discover even more extreme examples of unexpected phenomena. Weinvestigate strategies to identify novel objects and to contextualize themwithin large time-series data sets in order to facilitate the discovery of newclasses of objects, as well as the physical interpretation of their anomalousnature. We develop a method that combines tree-based and manifold-learningalgorithms for anomaly detection in order to perform two tasks: 1) identify andrank anomalous objects in a time-domain dataset; and 2) group those anomaliesaccording to their similarity in order to identify analogs. We achieve thelatter by combining an anomaly score from a tree-based method with adimensionality manifold-learning reduction strategy. Clustering in the reducedspace allows for the successful identification of anomalies and analogs. Wealso assess the impact of pre-processing and feature engineering schemes andinvestigate the astrophysical nature of the objects that our models identify asanomalous by augmenting the Kepler data with Gaia color and luminosityinformation. We find that multiple models, used in combination, are a promisingstrategy to identify novel light curves and light curve families.

A method for finding anomalous astronomical light curves and their analogs