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

Non-parametric morphology statistics have been used for decades to classifygalaxies into morphological types and identify mergers in an automated way. Inthis work, we assess how reliably we can identify galaxy post-mergers withnon-parametric morphology statistics. Low-redshift (z<0.2), recent(t_post-merger < 200 Myr), and isolated (r > 100 kpc) post-merger galaxies aredrawn from the IllustrisTNG100-1 cosmological simulation. Synthetic r-bandimages of the mergers are generated with SKIRT9 and degraded to various imagequalities, adding observational effects such as sky noise and atmosphericblurring. We find that even in perfect quality imaging, the individualnon-parametric morphology statistics fail to recover more than 55% of thepost-mergers, and that this number decreases precipitously with worsening imagequalities. The realistic distributions of galaxy properties in IllustrisTNGallow us to show that merger samples assembled using individual morphologystatistics are biased towards low mass, high gas fraction, and high mass ratio.However, combining all of the morphology statistics together using either alinear discriminant analysis or random forest algorithm increases thecompleteness and purity of the identified merger samples and mitigates biaswith various galaxy properties. For example, we show that in imaging similar tothat of the 10-year depth of the Legacy Survey of Space and Time (LSST), arandom forest can identify 89% of mergers with a false positive rate of 17%.Finally, we conduct a detailed study of the effect of viewing angle on mergerobservability and find that there may be an upper limit to merger recovery dueto the orientation of merger features with respect to the observer.

The limitations (and potential) of non-parametric morphology statistics for post-merger identification