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

Understanding large-angular-scale galactic foregrounds is crucial for futureCMB experiments aiming to detect $B$-mode polarization from primordialgravitational waves. Traditionally, the dust component has been separated usingits different frequency dependence. However, using non-CMB observations haspotential to increase fidelity and decrease the reconstruction noise. In thisexploratory paper we investigate the capability of galactic 21 cm observationsto predict the dust foreground in intensity. We train a neural network topredict the dust foreground as measured by the Planck Satellite from the fullvelocity data-cube of galactic 21 cm emission as measured by the HI4PI survey.We demonstrate that information in the velocity structure clearly improves thepredictive power over both a simple integrated emission model and a simplelinear model. The improvement is significant at arc-minute scales but moremodest at degree scales. This proof of principle on temperature data indicatesthat it might also be possible to improve foreground polarization templatesfrom the same input data.

Predicting CMB dust foreground using galactic 21 cm data