Thermonuclear (Type Ia) Supernovae and Progenitor Evolution

Calder, A. C.; Willcox, Donald E.; DeGrendele, Christopher J.; Shangase, D. M.; Zingale, M.; Townsley, Dean M.

doi:10.1088/1742-6596/1225/1/012002

Cited by 6 publications

(5 citation statements)

References 51 publications

(70 reference statements)

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“…The initial conditions are based on 1D stellar evolution models [10] and were implemented in MAESTROeX by a previous study [7,8]. We use a reaction network generated by the pynucastro python package [14].…”

Section: Numerical Methods and Simulation Setupmentioning

confidence: 99%

“…Previous work [4][5][6] on the convective Urca process has largely made either 1D or 2D approximations to model the turbulent convection, an inherently 3D process. We use the low Mach hydrodynamic code MAESTROeX to model convective Urca in 3D, building on an earlier study [7,8]. Additionally, we use weak reaction rates that depend on temperature and the electron density (which is important for this highly degenerate regime) [9].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of 3D Convective Urca Simulations to Changes in Urca Reactions

Boyd,

Smith Clark,

Calder

et al. 2024

J. Phys.: Conf. Ser.

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A proposed setting for thermonuclear (Type Ia) supernovae is a white dwarf that has gained mass from a companion to the point of carbon ignition in the core. There is a simmering phase in the early stages of burning that involves the formation and growth of a core convection zone. One aspect of this phase is the convective Urca process, a linking of weak nuclear reactions to convection that may alter the composition and structure of the white dwarf. Convective Urca is not well understood and requires 3D fluid simulations to realistically model. Additionally, the convection is relatively slow (Mach number less than 0.005) so a low-Mach method is needed to make simulating computationally feasible. Using the MAESTROeX low-Mach hydrodynamics code, we investigate recent changes to how the weak reactions are modeled in the convective Urca simulations. We present results that quantify the changes to the reaction rates and their impact on the evolution of the simulation.

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Section: Numerical Methods and Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensitivity of 3D Convective Urca Simulations to Changes in Urca Reactions

Boyd,

Smith Clark,

Calder

et al. 2024

J. Phys.: Conf. Ser.

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“…In pynucastro 2.0, we included the weak-rate tabular data [9,10] for nuclei A = 17 to 28. This was driven by our desire to simulate convective Urca [11]. From these tables, we extracted the β − -decay rate (β − ), electron-capture rate (e − ), gamma-energy rate (Γ e ), and the neutrino-energy rate loss (Γ ν ), reformatted in cgs units.…”

Section: Expanded Weak Ratesmentioning

confidence: 99%

pynucastro 2.1: an update on the development of a python library for nuclear astrophysics

Clark,

Johnson,

Chen

et al. 2024

J. Phys.: Conf. Ser.

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pynucastro1 is an open-source python library that provides visualization and analyze techniques to classify, construct, and evaluate nuclear reaction rates and networks. It provides tools that allow users to determine the importance of each rate in the network, based on a specified list of thermodynamic properties. Additionally, pynucastro can output a network in C++ or python for use in simulation codes, include the AMReX-Astrophysics simulation suite. We describe the changes in pynucastro since the last major release, including new capabilities that allow users to generate reduced networks and thermodynamic tables for conditions in nuclear statistical equilibrium.

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“…For C++ networks, AmrexAstroCxxNetwork automatically adds the calls to interpolate from the rate tables when filling the reaction rates. This network was used for convective Urca simulations (Calder et al 2019) with the MAESTROeX code (Fan et al 2019).…”

Section: Tabular Weak Ratesmentioning

confidence: 99%

pynucastro: A Python Library for Nuclear Astrophysics

Smith

Johnson

Chen

et al. 2023

ApJ

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We describe pynucastro 2.0, an open-source library for interactively creating and exploring astrophysical nuclear reaction networks. We demonstrate new methods for approximating rates and use detailed balance to create reverse rates, show how to build networks and determine whether they are appropriate for a particular science application, and discuss the changes made to the library over the past few years. Finally, we demonstrate the validity of the networks produced and share how we use pynucastro networks in simulation codes.

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Thermonuclear (Type Ia) Supernovae and Progenitor Evolution

Cited by 6 publications

References 51 publications

Sensitivity of 3D Convective Urca Simulations to Changes in Urca Reactions

Sensitivity of 3D Convective Urca Simulations to Changes in Urca Reactions

pynucastro 2.1: an update on the development of a python library for nuclear astrophysics

pynucastro: A Python Library for Nuclear Astrophysics

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