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

Many astronomical surveys are limited by the brightness of the sources, andgravitational-wave searches are no exception. The detectability ofgravitational waves from merging binaries is affected by the mass and spin ofthe constituent compact objects. To perform unbiased inference on thedistribution of compact binaries, it is necessary to account for this selectioneffect, which is known as Malmquist bias. Since systematic error from selectioneffects grows with the number of events, it will be increasingly important overthe coming years to accurately estimate the observational selection functionfor gravitational-wave astronomy. We employ density estimation methods toaccurately and efficiently compute the compact binary coalescence selectionfunction. We introduce a simple pre-processing method, which significantlyreduces the complexity of the required machine learning models. We demonstratethat our method has smaller statistical errors at comparable computational costthan the method currently most widely used allowing us to probe narrowerdistributions of spin magnitudes. The currently used method leaves $10-50\%$ ofthe interesting black hole spin models inaccessible; our new method can probe$>99\%$ of the models and has a lower uncertainty for $>80\%$ of the models.

Fast, flexible, and accurate evaluation of gravitational-wave Malmquist bias with machine learning