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Published September 20, 2020 | Published + Submitted
Journal Article Open

Art of Modeling Stellar Mergers and the Case of the B[e] Supergiant R4 in the Small Magellanic Cloud

Abstract

Most massive stars exchange mass with a companion, leading to evolution which is altered drastically from that expected of stars in isolation. Such systems result from unusual binary evolution pathways and can place stringent constraints on the physics of these interactions. We use the R4 binary system's B[e] supergiant, which has been postulated to be the product of a stellar merger, to guide our understanding of such outcomes by comparing observations of R4 to the results of simulating a merger with the 3D hydrodynamics code FLASH. Our approach tailors the simulation initial conditions to observed properties of R4 and implements realistic stellar profiles from the 1D stellar evolution code MESA onto the 3D grid, resolving the merger inspiral to within 0.02 R⊙. We map the merger remnant into MESA to track its evolution on the H-R diagram over a period of 10⁴ yr. This generates a model for a B[e] supergiant with stellar properties, age, and nebula structure in qualitative agreement with those of the R4 system. Our calculations provide evidence to support the idea that R4's B[e] supergiant was originally a member of a triple system in which the inner binary merged after its most massive member evolved off the main sequence, producing a new object of similar mass but significantly more luminosity than the A supergiant companion. The code framework presented in this paper, which was constructed to model tidal encounters, can be used to generate accurate models of a wide variety of merger stellar remnants.

Additional Information

© 2020 The American Astronomical Society. Received 2020 June 6; revised 2020 August 10; accepted 2020 August 12; published 2020 September 21. We gratefully acknowledge helpful discussions with D. Lee, S. de Mink, I. Mandel, P. Macias, A. Antoni, M. MacLeod, and J. Law-Smith. We also thank the referee for very useful comments and suggestions. We thank the Niels Bohr Institute for its hospitality while part of this work was completed and acknowledge the Kavli Foundation and the DNRF for supporting the 2017 Kavli Summer Program. E.R.-R. and R.W.E. thank the David and Lucile Packard Foundation, the Heising-Simons Foundation, and the Danish National Research Foundation (DNRF132) for support. R.W.E. is supported by the Eugene V. Cota-Robles Fellowship and National Science Foundation Graduate Research Fellowship Program (Award #1339067). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The software used in this work was developed in part by the DOE NNSA ASC- and DOE Office of Science ASCR-supported Flash Center for Computational Science at the University of Chicago. Resources supporting this work were provided by the University of Copenhagen high-performance computing cluster funded by a grant from VILLUM FONDEN (project number 16599).

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Published - Wu_2020_ApJ_901_44.pdf

Submitted - 2006.01940.pdf

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