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

Modern radio interferometers enable observations of spectral lines withunprecedented spatial resolution and sensitivity. In spite of these technicaladvances, many lines of interest are still at best weakly detected andtherefore necessitate detection and analysis techniques specialized for the lowsignal-to-noise ratio (SNR) regime. Matched filters can leverage knowledge ofthe source structure and kinematics to increase sensitivity of spectral lineobservations. Application of the filter in the native Fourier domain improvesSNR while simultaneously avoiding the computational cost and ambiguitiesassociated with imaging, making matched filtering a fast and robust method forweak spectral line detection. We demonstrate how an approximate matched filtercan be constructed from a previously observed line or from a model of thesource, and we show how this filter can be used to robustly infer a detectionsignificance for weak spectral lines. When applied to ALMA Cycle 2 observationsof CH3OH in the protoplanetary disk around TW Hya, the technique yields a ~53%SNR boost over aperture-based spectral extraction methods, and we show that aneven higher boost will be achieved for observations at higher spatialresolution. A Python-based open-source implementation of this technique isavailable under the MIT license at https://github.com/AstroChem/VISIBLE.

Detecting Weak Spectral Lines in Interferometric Data through Matched Filtering