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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Identifying the angular degrees $l$ of oscillation modes is essential forasteroseismology and depends on visual tagging before fitting power spectra ina so-called peakbagging analysis. In oscillating subgiants, radial ($l$= 0)mode frequencies distributed linearly in frequency, while non-radial ($l$ >= 1)modes are p-g mixed modes that having a complex distribution in frequency,which increased the difficulty of identifying $l$. In this study, we trained a1D convolutional neural network to perform this task using smoothed oscillationspectra. By training simulation data and fine-tuning the pre-trained network,we achieved a 95 per cent accuracy on Kepler data.

Mode Angular Degree Identification in Subgiant Stars with Convolutional Neural Networks based on Power Spectrum