English

Modules for Experiments in Stellar Astrophysics (MESA): Time-Dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure

Solar and Stellar Astrophysics 2023-03-08 v2 Earth and Planetary Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics

Abstract

We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (MESA). The new auto_diff module implements automatic differentiation in MESA, an enabling capability that alleviates the need for hard-coded analytic expressions or finite difference approximations. We significantly enhance the treatment of the growth and decay of convection in MESA with a new model for time-dependent convection, which is particularly important during late-stage nuclear burning in massive stars and electron degenerate ignition events. We strengthen MESA's implementation of the equation of state, and we quantify continued improvements to energy accounting and solver accuracy through a discussion of different energy equation features and enhancements. To improve the modeling of stars in MESA we describe key updates to the treatment of stellar atmospheres, molecular opacities, Compton opacities, conductive opacities, element diffusion coefficients, and nuclear reaction rates. We introduce treatments of starspots, an important consideration for low-mass stars, and modifications for superadiabatic convection in radiation-dominated regions. We describe new approaches for increasing the efficiency of calculating monochromatic opacities and radiative levitation, and for increasing the efficiency of evolving the late stages of massive stars with a new operator split nuclear burning mode. We close by discussing major updates to MESA's software infrastructure that enhance source code development and community engagement.

Keywords

Cite

@article{arxiv.2208.03651,
  title  = {Modules for Experiments in Stellar Astrophysics (MESA): Time-Dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure},
  author = {Adam S. Jermyn and Evan B. Bauer and Josiah Schwab and R. Farmer and Warrick H. Ball and Earl P. Bellinger and Aaron Dotter and Meridith Joyce and Pablo Marchant and Joey S. G. Mombarg and William M. Wolf and Tin Long Sunny Wong and Giulia C. Cinquegrana and Eoin Farrell and R. Smolec and Anne Thoul and Matteo Cantiello and Falk Herwig and Odette Toloza and Lars Bildsten and Richard H. D. Townsend and F. X. Timmes},
  journal= {arXiv preprint arXiv:2208.03651},
  year   = {2023}
}

Comments

50 pages, 29 figures; Accepted to ApJS