Cococubed.com
Home Astronomy research   Software Infrastructure:      MESA      FLASH-X      STARLIB      MESA-Web      starkiller-astro      My instruments   White dwarf supernova:      Stable nickel production      Remnant metallicities      Colliding white dwarfs      Merging white dwarfs      Ignition conditions      Metallicity effects      Central density effects      Detonation density effects      Tracer particle burning      Subsonic burning fronts      Supersonic burning fronts      W7 profiles   Massive star supernova:      Rotating progenitors      3D evolution      26Al & 60Fe      44Ti, 60Co & 56Ni      Yields of radionuclides      Effects of 12C +12C      SN 1987A light curve      Constraints on Ni/Fe ratios      An r-process   Neutron Stars and Black Holes:      Black Hole mass spectrum      Compact object IMF      He burn on neutron stars   Stars:      Variable white dwarfs      Pop III with JWST      Micronovae      Neutrino HR diagram      Monte Carlo massive stars      Pre-supernova neutrinos      Pre-supernova variations      Monte Carlo white dwarfs      SAGB stars      Nugrid Yields I      Classical novae      He shell convection      Presolar grains      BBFH at 40 years   Chemical Evolution:      Iron Pseudocarbynes      Radionuclides in the 2020s      Hypatia catalog      Zone models H to Zn      Mixing ejecta      γ-rays within 100 Mpc   Thermodynamics & Networks      Stellar EOS      12C(α,γ)16O Rate      Proton-rich NSE      Reaction networks      Bayesian reaction rates   Verification Problems:      Validating an astro code      Su-Olson      Cog8      Mader      RMTV      Sedov      Noh Software instruments Presentations Illustrations cococubed YouTube Bicycle adventures Public Outreach Education materials 2022 ASU Solar Systems Astronomy 2022 ASU Energy in Everyday Life AAS Journals AAS YouTube 2022 Earendel, A Highly Magnified Star 2022 TV Columbae, Micronova 2022 White Dwarfs and 12C(α,γ)16O 2022 MESA VI 2022 MESA in Don't Look Up 2022 MESA Marketplace 2022 MESA Summer School 2022 MESA Classroom 2021 Bill Paxton, Tinsley Prize Contact: F.X.Timmes my one page vitae, full vitae, research statement, and teaching statement.	STARLIB is avaliable at https://starlib.github.io/Rate-Library/ The Impact of Nuclear Reaction Rate Uncertainties On The Evolution of Core-Collapse Supernova Progenitors (2018) We investigate properties of massive stars with respect to the composite uncertainties in the reaction rates using MESA and STARLIB. This is the first Monte Carlo massive star evolution study that use complete stellar models. Properties Of Carbon-Oxygen White Dwarfs From Monte Carlo Stellar Models (2016) We investigate properties of carbon-oxygen white dwarfs with respect to the composite uncertainties in the reaction rates using MESA and STARLIB. This is the first Monte Carlo stellar evolution study that use complete stellar models. Bayesian Estimation Of Thermonuclear Reaction Rates (2016) Estimating non-resonant astrophysical S-factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied in the past to this problem, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extra-solar planets, gravitational waves, and type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. In this article we present astrophysical S-factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the d(p,γ)3He, 3He(3He,2p)4He, and 3He(α,γ)7Be reactions, important for deuterium burning, solar neutrinos, and big bang nucleosynthesis. Statistical Methods for Thermonuclear Reaction Rates and Nucleosynthesis Simulations (2015) Rigorous statistical methods for estimating thermonuclear reaction rates and nucleosynthesis are becoming increasingly established in nuclear astrophysics. The main challenge being faced is that experimental reaction rates are highly complex quantities derived from a multitude of different measured nuclear parameters (e.g., astrophysical S-factors, resonance energies and strengths, particle and γ-ray partial widths). In this article we discuss the application of the Monte Carlo method to two distinct, but related, questions. First, given a set of measured nuclear parameters, how can one best estimate the resulting thermonuclear reaction rates and associated uncertainties? Second, given a set of appropriate reaction rates, how can one best estimate the abundances from nucleosynthesis (i.e., reaction network) calculations? The techniques described here provide probability density functions that can be used to derive statistically meaningful reaction rates and final abundances for any desired coverage probability. Examples are given for applications to s-process neutron sources, core-collapse supernovae, classical novae, and big bang nucleosynthesis. STARLIB: A Next-Generation Reaction-Rate Library for Nuclear Astrophysics (2013) STARLIB, discussed in this article, is a next-generation, all-purpose nuclear reaction-rate library. For the first time, a library provides the rate probability density at all temperature grid points for convenient implementation in models of stellar phenomena. The recommended rate and its associated uncertainties are also included. Currently, uncertainties are absent from all other rate libraries, and, although estimates have been attempted in previous evaluations and compilations, these are generally not based on rigorous statistical definitions. A common standard for deriving uncertainties is clearly warranted. STARLIB represents a first step in addressing this deficiency by providing a tabular, up-to-date database that supplies not only the rate and its uncertainty but also its distribution. Because a majority of rates are lognormally distributed, this allows the construction of rate probability densities from the columns of STARLIB. This structure is based on a recently suggested Monte Carlo method to calculate reaction rates, where uncertainties are rigorously defined. In STARLIB, experimental rates are supplemented with: (i) theoretical TALYS rates for reactions for which no experimental input is available, and (ii) laboratory and theoretical weak rates. STARLIB includes all types of reactions of astrophysical interest to Z=83, such as (p,γ), (p,α), (α,n), and corresponding reverse rates. Strong rates account for thermal target excitations. Here, we summarize our Monte Carlo formalism, introduce the library, compare methods of correcting rates for stellar environments, and discuss how to implement our library in Monte Carlo nucleosynthesis studies.