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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Rejecting cosmic rays (CRs) is essential for the scientific interpretation ofCCD-captured data, but detecting CRs in single-exposure images has remainedchallenging. Conventional CR detectors require experimental parameter tuningfor different instruments, and recent deep learning methods only produceinstrument-specific models that suffer from performance loss on telescopes notincluded in the training data. We present Cosmic-CoNN, a generic CR detectordeployed for 24 telescopes at the Las Cumbres Observatory, which is madepossible by the three contributions in this work: 1) We build a large anddiverse ground-based CR dataset leveraging thousands of images from a globaltelescope network. 2) We propose a novel loss function and a neural networkoptimized for telescope imaging data to train generic CR detection models. At95% recall, our model achieves a precision of 93.70% on Las Cumbres imagingdata and maintains a consistent performance on new ground-based instrumentsnever used for training. Specifically, the Cosmic-CoNN model trained on the LasCumbres CR dataset maintains high precisions of 92.03% and 96.69% on GeminiGMOS-N/S 1x1 and 2x2 binning images, respectively. 3) We build a suite of toolsincluding an interactive CR mask visualization and editing interface, consolecommands, and Python APIs to make automatic, robust CR detection widelyaccessible by the community of astronomers. Our dataset, open-source codebase,and trained models are available at https://github.com/cy-xu/cosmic-conn.

Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit