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

Along with the nearing completion of the Square Kilometre Array (SKA), comesan increasing demand for accurate and reliable automated solutions to extractvaluable information from the vast amount of data it will allow acquiring.Automated source finding is a particularly important task in this context, asit enables the detection and classification of astronomical objects.Deep-learning-based object detection and semantic segmentation models haveproven to be suitable for this purpose. However, training such deep networksrequires a high volume of labeled data, which is not trivial to obtain in thecontext of radio astronomy. Since data needs to be manually labeled by experts,this process is not scalable to large dataset sizes, limiting the possibilitiesof leveraging deep networks to address several tasks. In this work, we proposeRADiff, a generative approach based on conditional diffusion models trainedover an annotated radio dataset to generate synthetic images, containing radiosources of different morphologies, to augment existing datasets and reduce theproblems caused by class imbalances. We also show that it is possible togenerate fully-synthetic image-annotation pairs to automatically augment anyannotated dataset. We evaluate the effectiveness of this approach by training asemantic segmentation model on a real dataset augmented in two ways: 1) usingsynthetic images obtained from real masks, and 2) generating images fromsynthetic semantic masks. We show an improvement in performance when applyingaugmentation, gaining up to 18% in performance when using real masks and 4%when augmenting with synthetic masks. Finally, we employ this model to generatelarge-scale radio maps with the objective of simulating Data Challenges.

RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation