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

A stochastic gravitational-wave background (SGWB) is expected from thesuperposition of a wide variety of independent and unresolved astrophysical andcosmological sources from different stages in the evolution of the Universe.Radiometric techniques are used to make sky maps of anisotropies in the SGWB bycross-correlating data from pairs of detectors. The conventional searches canbe made hundreds of times faster through the folding mechanism introducedrecently. Here we present a newly developed algorithm to perform the SGWBsearches in a highly efficient way. Taking advantage of the compactness of thefolded data we replaced the loops in the pipeline with matrix multiplications.We also incorporated well-known HEALPix pixelization tools for furtherstandardization and optimization. Our Python-based implementation of thealgorithm is available as an open source package ${\tt PyStoch}$. Folding and${\tt PyStoch}$ together has made the radiometer analysis $\textit{a fewthousand times}$ faster; it is now possible to make all-sky maps of astochastic background in just a few minutes on an ordinary laptop. Moreover,${\tt PyStoch}$ generates a skymap at every frequency bin as an intermediatedata product. These techniques have turned SGWB searches very convenient andwill make computationally challenging analyses like blind all-sky narrowbandsearch feasible.

Very fast stochastic gravitational wave background map making using folded data