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

In radio interferometry, observed visibilities are intrinsically sampled atsome interval in time and frequency. Modern interferometers are capable ofproducing data at very high time and frequency resolution; practical limits onstorage and computation costs require that some form of data compression beimposed. The traditional form of compression is a simple averaging of thevisibilities over coarser time and frequency bins. This has an undesired sideeffect: the resulting averaged visibilities "decorrelate", and do sodifferently depending on the baseline length and averaging interval. Thistranslates into a non-trivial signature in the image domain known as"smearing", which manifests itself as an attenuation in amplitude towardsoff-centre sources. With the increasing fields of view and/or longer baselinesemployed in modern and future instruments, the trade-off between data rate andsmearing becomes increasingly unfavourable. In this work we investigatealternative approaches to low-loss data compression. We show that averaging ofthe visibility data can be treated as a form of convolution by a boxcar-likewindow function, and that by employing alternative baseline-dependent windowfunctions a more optimal interferometer smearing response may be induced. Inparticular, we show improved amplitude response over a chosen field ofinterest, and better attenuation of sources outside the field of interest. Themain cost of this technique is a reduction in nominal sensitivity; weinvestigate the smearing vs. sensitivity trade-off, and show that in certainregimes a favourable compromise can be achieved. We show the application ofthis technique to simulated data from the Karl G. Jansky Very Large Array (VLA)and the European Very-long-baseline interferometry Network (EVN).

Using baseline-dependent window functions for data compression and field-of-interest shaping in radio interferometry