Published December 28, 2025 | Version v1
Dataset Open

Massive Stellar Cannibals: How Stellar Mergers Drive Mass-Loss in Extremely Massive Stars

  • 1. University of Geneva
  • 2. Gravitational Wave Science center
  • 3. Gravitational Wave Science Center
  • 4. ROR icon Institut de Recherche en Astrophysique et Planétologie
  • 5. Yale University

Description

Simulation outputs for stellar mergers of extremely massive stars using MESA.

Description:

  • results_1e3Msun.tar.zst: Compressed data including MESA history and profile files for stellar mergers involving a 1000 solar mass star.
  • results_3e3Msun.tar.zst: Compressed data including MESA history and profile files for stellar mergers involving a 3000 solar mass star.
  • results_5e3Msun.tar.zst: Compressed data including MESA history and profile files for stellar mergers involving a 5000 solar mass star.
  • merger_discrimination.txt: Table summarizing the predicted mass loss produced during a stellar merger. The columns are:
  • M_EMS: Extremely massive star mass in solar masses.
    • M_COMP: Mass of the companion star in solar masses.
    • L_R: Maximum latus rectum (in solar radii) to produce a direct merger for mergers with eccentricity of unity. Interactions with a latus rectum above this value will produce orbital scattering instead.
    • R_EMS: Radius of the extremely massive star in solar radii.
    • log10(E_orb): log10 of the orbital energy (erg) lost during the interaction, either during the merger or orbital scattering.
    • M_UNB: Unbound mass in solar masses. This is the predicted mass loss.
    • T_MERGER: Timescale in yr from the start of the interaction until either both stars merge or the companion exits the EMS.

Files

merger_discrimination.txt

Files (89.0 GB)

Name Size Download all
md5:4f0649d0935f70b6c2b42a53441574f7
807.5 kB Preview Download
md5:556497ab8b011397119a5c767460b088
30.9 GB Download
md5:fd58ee270fe72af398d4ac230652e21e
33.7 GB Download
md5:b77c012fb0e795a9744a37fb96a7edd7
24.4 GB Download

Additional details

Funding

Swiss National Science Foundation
GW-Learn: Deciphering the Gravitation-Wave Universe using the Next-Generation Observatories and Machine Learning 213497

Software