Flash-X: A multiphysics simulation software instrument

Dubey, Anshu; Weide, Klaus; O’Neal, Jared; Dhruv, Akash; Couch, Sean M.; Harris, J. Austin; Klosterman, Tom; Jain, Rajeev; Rudi, Johann; Messer, Bronson; Pajkos, Michael A.; Carlson, Joe; Chu, Ran; Wahib, Mohamed; Chawdhary, Saurabh; Ricker, P. M.; Lee, Dong‐Wook; Antypas, Katie; Riley, Katherine; Daley, C.; Ganapathy, Murali K.; Timmes, F. X.; Townsley, Dean M.; Vanella, Marcos; Bachan, John; Rich, Paul M.; Kumar, Shravan; Endeve, Eirik; Hix, W. R.; Mezzacappa, Anthony; Papatheodore, Thomas

doi:10.1016/j.softx.2022.101168

Cited by 11 publications

(5 citation statements)

References 32 publications

(42 reference statements)

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“…The collapse of the progenitors' cores and subsequent explosions were simulated with FLASH 11 (Fryxell et al 2000;Dubey et al 2009Dubey et al , 2022 in Couch et al (2020) using the STIR turbulence-aided explosion model. Turbulence has been shown to be key in simulating successful, realistic explosions (see, e.g., Burrows et al 1995;Murphy & Meakin 2011;Couch & Ott 2015;Mabanta & Murphy 2018).…”

Section: Methods and Input Datamentioning

confidence: 99%

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Barker

O’Connor

Couch

2023

ApJL

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Connecting observations of core-collapse supernova explosions to the properties of their massive star progenitors is a long-sought, and challenging, goal of supernova science. Recently, Barker et al. presented bolometric light curves for a landscape of progenitors from spherically symmetric neutrino-driven core-collapse supernova (CCSN) simulations using an effective model. They find a tight relationship between the plateau luminosity of the Type II-P CCSN light curve and the terminal iron-core mass of the progenitor. Remarkably, this allows us to constrain progenitor properties with photometry alone. We analyze a large observational sample of Type II-P CCSN light curves and estimate a distribution of iron-core masses using the relationship of Barker et al. The inferred distribution matches extremely well with the distribution of iron-core masses from stellar evolutionary models and namely, contains high-mass iron cores that suggest contributions from very massive progenitors in the observational data. We use this distribution of iron-core masses to infer minimum and maximum masses of progenitors in the observational data. Using Bayesian inference methods to locate optimal initial mass function parameters, we find M min = 9.8 − 0.27 + 0.37 and M max = 24.0 − 1.9 + 3.9 solar masses for the observational data.

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Section: Methods and Input Datamentioning

confidence: 99%

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Barker

O’Connor

Couch

2023

ApJL

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“…• FLASH [163]: a multi-physics code that, when employed for CCSNe simulations, typically uses Adaptive Mesh Refinement (AMR) for the hydrodynamics, an approximate treatment of general relativistic effects following Case A of Marek et al [162], and a two-moment M1 scheme [156] for multidimensional neutrino transport. Most versions of the code do not include effects of nuclear burning, although a more modern version of the code, i.e., FLASH-X, is currently under development [164]; • 3DnSNeIDSA [34]: it uses an approximate treatment of general relativistic effects following Case A of Marek et al [162], and neutrino transport is solved using a spectral method based on the isotropic source diffusion approximation (ISDA) [165], where multidimensional effects are treated using a ray-by-ray approximation. The most recent version of the code is also able to solve the magnetohydrodynamic (MHD) equations [166].…”

Section: State-of-the-art Ccsne Codesmentioning

confidence: 99%

“…CCSNe are one of the possible production sites of 35 rare neutron-deficient isotopes of elements heavier than Fe (namely: 74 Se, 78 Kr, 84 Sr, 92,94 Mo, 96,98 Ru, 102 Pd, 106,108 Cd, 112,114,115 Sn, 113 In, 120 Te, 124,126 Xe, 130,132 Ba, 136,138 Ce, 138 La, 144 Sm, 152 Gd, 156,158 Dy, 162,164 Er, 168 Yb, 174 Hf, 180 Ta, 180 W, 184 Os, 190 Pt, and 196 Hg), the so-called p-nuclei [228][229][230][231][232][233]. These peculiar nuclei can not be synthesized by neutron capture processes.…”

Section: The Production Of P-nuclei: the γ-Process In Ccsnementioning

confidence: 99%

The Physics of Core-Collapse Supernovae: Explosion Mechanism and Explosive Nucleosynthesis

Boccioli,

Roberti

2024

Universe

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Recent developments in multi-dimensional simulations of core-collapse supernovae have considerably improved our understanding of this complex phenomenon. In addition to that, one-dimensional (1D) studies have been employed to study the explosion mechanism and its causal connection to the pre-collapse structure of the star, as well as to explore the vast parameter space of supernovae. Nonetheless, many uncertainties still affect the late stages of the evolution of massive stars, their collapse, and the subsequent shock propagation. In this review, we will briefly summarize the state-of-the-art of both 1D and 3D simulations and how they can be employed to study the evolution of massive stars, supernova explosions, and shock propagation, focusing on the uncertainties that affect each of these phases. Finally, we will illustrate the typical nucleosynthesis products that emerge from the explosion.

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“…Simulation sofware instruments like Flash-X (Dubey et al, 2022) store output in the form of Hierarchical Data Format (HDF5) datasets. Each dataset is often gigabytes (GB) in size and requires cache efficient techniques to enable its integration with Python packages.…”

Section: Statement Of Needmentioning

confidence: 99%

BoxKit: A Python library to manage analysis of block-structured simulation datasets

Dhruv

2023

JOSS

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BoxKit is a library that provides building blocks to parallelize and scale data science, statistical analysis, and machine learning applications for block-structured simulation datasets. Spatial data from simulations can be accessed and managed using tools available in this library to interface with packages like SciKit, PyTorch, and OpticalFlow for post-processing and analysis.Dhruv. ( 2023). BoxKit: A Python library to manage analysis of block-structured simulation datasets.

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Flash-X: A multiphysics simulation software instrument

Cited by 11 publications

References 32 publications

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

The Physics of Core-Collapse Supernovae: Explosion Mechanism and Explosive Nucleosynthesis

BoxKit: A Python library to manage analysis of block-structured simulation datasets

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