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/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Large surveys using modern telescopes are producing images that areincreasing exponentially in size and quality. Identifying objects in thegenerated images by visual recognition is time-consuming and labor-intensive,while classifying the extracted radio sources is even more challenging. Toaddress these challenges, we develop a deep learning-based radio sourcedetector, named \textsc{HeTu}, which is capable of rapidly identifying andclassifying radio sources in an automated manner for both compact and extendedradio sources. \textsc{HeTu} is based on a combination of a residual network(ResNet) and feature pyramid network (FPN). We classify radio sources into fourclasses based on their morphology. The training images are manually labeled anddata augmentation methods are applied to solve the data imbalance between thedifferent classes. \textsc{HeTu} automatically locates the radio sources in theimages and assigns them to one of the four classes. The experiment on thetesting dataset shows an average operation time of 5.4 millisecond per imageand a precision of 99.4\% for compact point-like sources and 98.1\% fordouble-lobe sources. We applied \textsc{HeTu} to the images obtained from theGaLactic and the Galactic Extragalactic All-Object Murchison Wide-field Array(GLEAM) survey project. More than 96.9\% of the \textsc{HeTu}-detected compactsources are matched compared to the source finding software used in the GLEAM.We also detected and classified 2,298 extended sources (includingFanaroff-Riley type I and II sources, and core-jet sources) above $5\sigma$.The cross-matching rates of extended sources are higher than 97\%, showingexcellent performance of \textsc{HeTu} in identifying extended radio sources.\textsc{HeTu} provides an efficient tool for radio source finding andclassification and can be applied to other scientific fields.

Artificial intelligence for celestial object census: the latest technology meets the oldest science