Third Party Data

Third Party Data#

pynucastro incorporates the following publicly-available third-party data. Links to this data as well as citations to the relevant publications are as follows.

Reaction rates#

Nuclear reaction rates from JINA Reaclib#

The reaction rate parameterizations in pynucastro/library were obtained from the JINA Reaclib database, Cyburt et al. [2010].

Tabulated weak nuclear reaction rates#

The weak rates come from several different sources, each of which focuses on a particular range of masses.

Warning

In the C++ networks, no error is produced if you try to evaluate a rate outside of the table limits. Instead, an extrapolation is done using the data at the edge of the table.

  • For nuclei with \(A = 17\) to \(28\) we use the weak rates from Suzuki et al. [2016]. The data tables were obtained from https://www.phys.chs.nihon-u.ac.jp/suzuki/data2/link.html.

    The density (g/cm\(^3\)) and temperature (K) ranges of the rates are:

    • \(7 \le \log_{10}(\rho Y_e) \le 11\)

    • \(7 \le \log_{10}(T) \le 9.65\)

    Note

    The paper [Suzuki et al., 2016] says that the rates are evaluated are in the range \(8 \le \log_{10}(\rho Y_e) \le 11\), but the tables provided have the lower limit as \(\log_{10}(\rho Y_e) = 7\).

  • For nuclei with \(A = 21\) to \(60\) we can use the weak rates from Fuller et al. [1982].

    The density (g/cm\(^3\)) and temperature (K) ranges of the rates are:

    • \(1 \le \log_{10}(\rho Y_e) \le 11\)

    • \(7 \le \log_{10}(T) \le 11\)

  • For nuclei with \(A = 45\) to \(65\) we use the weak rates from Langanke and Martínez-Pinedo [2001]. That journal link includes the data tables.

    The density (g/cm\(^3\)) and temperature (K) ranges of the rates are:

    • \(1 \le \log_{10}(\rho Y_e) \le 11\)

    • \(7 \le \log_{10}(T) \le 11\)

    Note

    These rates should be preferred to the FFN rates where there is overlap.

Physical constants#

We use the scipy.constants module from SciPy to get all the physical constants. This in turn gets the constants from the CODATA recommended values (for SciPy versions 1.14 and earlier, this is CODATA 2018, Tiesinga et al. [2021]; for SciPy 1.15 they are CODATA 2022).

Nuclei properties#

We get the basic nuclear properties from the Nubase 2020 evaluation Kondev et al. [2021]. This is available online at Nuclear Data Services. We are currently using the file nubase_4.mas20.txt.

In particular, we get the mass excesses, \(\Delta m\), and spins from there. We then compute mass of the nucleus as:

\[m = \Delta M + A m_u\]

and the binding energies from the mass excesses as:

\[B = Z m_H + N m_n - (A m_u + \Delta m)\]

where \(m_H\) is the mass of the hydrogen atom, computed from the mass excess of 1H listed in the table. This is consistent with the discussion in section 2 of the AME 2020 paper [Huang et al., 2021], and these numbers match the binding energies computed in the AME tables to the uncertainty in the nuclear masses.

Binding energies are also computed and tablulated in the AME mass evaluation (see AME2020 mass table). But note that the Nubase evaluation seems to more closely follow the “rounded” version of the table AME2020 rounded mass table. The rounding procedure is discussed in Table I on the AME 2020 paper II (also see the Nubase2020 paper, Table I).

Partition functions#

We use the tabulated partition functions from the following sources:

  • Rauscher et al. [1997]

  • Rauscher [2003]

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