1D stellar mergers: entropy sorting and PyMMAMS
Max Heller, Fabian R. N. Schneider, Jan Henneco, Vincent A. Bronner, Mike Y. M. Lau

TL;DR
This paper compares 1D entropy sorting methods to 3D simulations for modeling stellar mergers, demonstrating that calibrated 1D models can effectively reproduce complex merger outcomes and highlighting the importance of shock-heating in post-merger structure prediction.
Contribution
The study introduces and validates a Python-based 1D merger prescription that closely matches 3D simulation results, improving the efficiency of stellar merger modeling.
Findings
PM reproduces 3D merger outcomes better than ES
Shock-heating calibration improves entropy profile accuracy
1D methods can effectively approximate 3D stellar mergers
Abstract
Stellar multiple systems are the norm, not the exception, with many systems undergoing interaction phases during their lifetimes. A subset of these interactions can lead to stellar mergers, where the two components of a stellar binary system come close enough to coalesce into a single star. Accurately modeling stellar mergers requires computationally expensive 3D methods, which are not suited for exploring large parameter spaces as required e.g., by population synthesis studies. In this work, we compare two 1D prescriptions based on the concept of entropy sorting to their 3D counterparts. We employ a basic entropy sorting method ('ES'), which builds the merger remnant by sorting the progenitor stars' shells by increasing entropy, and a Python version of the 'Make Me A Massive Star' code ('PM'), which additionally applies a shock-heating prescription calibrated on SPH simulations of…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Gamma-ray bursts and supernovae · Stellar, planetary, and galactic studies
