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

Automated planetary transit detection has become vital to prioritizecandidates for expert analysis given the scale of modern telescopic surveys.While current methods for short-period exoplanet detection work effectively dueto periodicity in the light curves, there lacks a robust approach for detectingsingle-transit events. However, volunteer-labelled transits recently collectedby the Planet Hunters TESS (PHT) project now provide an unprecedentedopportunity to investigate a data-driven approach to long-period exoplanetdetection. In this work, we train a 1-D convolutional neural network toclassify planetary transits using PHT volunteer scores as training data. Wefind using volunteer scores significantly improves performance over syntheticdata, and enables the recovery of known planets at a precision and ratematching that of the volunteers. Importantly, the model also recovers transitsfound by volunteers but missed by current automated methods.

Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels