$N$-body modelling of the ED-2 stream progenitor shows Gaia BH3's formation involved dynamical interactions
Abstract
Context. The Gaia collaboration announced the discovery of a binary of a massive black hole (33 M$_\odot$) with a low-mass giant star (Gaia BH3) in the ED-2 stellar stream. The properties of this binary, as well as its position in the stream, challenge a formation scenario invoking only isolated binary evolution. Aims. We aim to quantify the importance of cluster dynamics in the formation of Gaia BH3 in the progenitor cluster of the ED-2 stream. Methods. We perform detailed N-body simulations of the progenitor cluster of the ED-2 stream, including the effects of single and binary stellar evolution. We compare these simulations to observations of the ED-2 stream and the properties of Gaia BH3. Results. We determine that Gaia BH3 most likely formed as an exchange binary which underwent multiple strong dynamical interactions. We highlight the importance of cluster dynamics in assembling Gaia BH3, and disfavour a formation scenario where it evolved unperturbed by dynamical interactions. Conclusions. The role of dynamics should be considered when interpreting the properties of the population of star-black hole binaries found in the next Gaia Data Release.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.