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

We address two important questions in gravitational-wave astronomy. What isthe astrophysical formation scenario leading to black-hole binary mergers? Didsome of the merging black holes form hierarchically through previousgenerations of mergers? Leveraging fast-to-generate astrophysical simulationsfrom the rapster code and a random forest algorithm, we develop a pipeline toaccurately classify the most likely generation and formation scenario ofdynamically formed BHs on an event-by-event basis. We test our framework onfour merger events with features suggesting a dynamical origin: the large totalmass event GW190521, GW190412 (with large mass asymmetry), and two events witheffective spins antialigned with the orbital angular momentum (GW191109 andGW200225). Within the models we consider, and assuming these events to beformed dynamically, we find that one of the component black holes in GW190521formed from a previous merger with high probability ($\gtrsim 85\%$). GW190521,GW191109 and GW200225 are compatible with formation through three-bodyinteractions, while the most likely formation channel for GW190412 are two-bodycaptures. We also rule out that GW191109 contains only first-generation blackholes with a probability of 97$\%$. Our pipeline could be useful to identifythe evolutionary path of individual GW observations once it is trained on morecomprehensive sets of binary formation simulations.

Classifying the generation and formation channels of individual LIGO-Virgo-KAGRA observations from dynamically formed binaries