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

The VISTA Variables in the Via Lactea (VVV) survey and its extension, havebeen monitoring about 560 square degrees of sky centred on the Galactic bulgeand inner disc for nearly a decade. The photometric catalogue contains of order10$^9$ sources monitored in the K$_s$ band down to 18 mag over hundreds ofepochs from 2010-2019. Using these data we develop a decision tree classifierto identify microlensing events. As inputs to the tree, we extract a fewphysically motivated features as well as simple statistics ensuring a good fitto a microlensing model both on and off the event amplification. This producesa fast and efficient classifier trained on a set of simulated microlensingevents and catacylsmic variables, together with flat baseline light curvesrandomly chosen from the VVV data. The classifier achieves 97 per cent accuracyin identifying simulated microlensing events in a validation set. We run theclassifier over the VVV data set and then visually inspect the results, whichproduces a catalogue of 1,959 microlensing events. For these events, we providethe Einstein radius crossing time via a Bayesian analysis. The spatialdependence on recovery efficiency of our classifier is well characterised, andthis allows us to compute spatially resolved completeness maps as a function ofEinstein crossing time over the VVV footprint. We compare our approach toprevious microlensing searches of the VVV. We highlight the importance ofBayesian fitting to determine the microlensing parameters for events withsurveys like VVV with sparse data.

A microlensing search of 700 million VVV light curves