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

The Bright Transient Survey (BTS) relies on visual inspection ("scanning") toselect sources for accomplishing its mission of spectroscopically classifyingall bright extragalactic transients found by the Zwicky Transient Facility(ZTF). We present $\texttt{BTSbot}$, a multi-input convolutional neuralnetwork, which provides a bright transient score to individual ZTF detectionsusing their image data and 14 extracted features. $\texttt{BTSbot}$ eliminatesthe need for scanning by automatically identifying and requesting follow-upobservations of new bright ($m\,<18.5\,\mathrm{mag}$) transient candidates.$\texttt{BTSbot}$ outperforms BTS scanners in terms of completeness (99% vs.95%) and identification speed (on average, 7.4 hours quicker).

$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to  Automate and Expedite Bright Transient Identification for the Zwicky  Transient Facility