add refs to pynucastro

Docs/source/networks.rst

@@ -22,7 +22,7 @@ Microphysics knows the properties of the fluid.
 .. note::
 
    Many of the networks here are generated using `pynucastro
-   <https://pynucastro.github.io/>`_ using the ``AmrexAstroCxxNetwork``
+   <https://pynucastro.github.io/>`_ :cite:`pynucastro, pynucastro2` using the ``AmrexAstroCxxNetwork``
    class.
 
 ``general_null``

Docs/source/nse_tabular.rst

@@ -34,14 +34,19 @@ Composition and EOS
 ===================
 
 The NSE table was generated using `pynucastro
-<https://pynucastro.github.io/pynucastro/>` using 96 nuclei and
-electron/positron capture/decay rates from :cite:`langanke:2001`.  The
-table takes $Y_e$ as the primary composition variable and provides a
-set of mass fractions that is mapped into those used by ``aprox19``.
-Using the value allows us to attain a lower :math:`Y_e` than
-``aprox19`` can represent.
-
-For this reason, when we are using the NSE network, we always take the
+<https://pynucastro.github.io/pynucastro/>`_ :cite:`pynucastro,
+pynucastro2`, using 96 nuclei and electron/positron capture/decay
+rates from :cite:`langanke:2001`.  The table takes $Y_e$ as the
+primary composition variable and provides a set of mass fractions that
+is mapped into those used by ``aprox19``.  Using the value allows us
+to attain a lower :math:`Y_e` than ``aprox19`` can represent.
+
+.. note::
+
+   The full details of the NSE table are provided in :cite:`sdc-nse`.
+   The table can be regenerated using the script ``nse_tabular/make_nse_table.py``.
+
+When we are using the NSE network, we always take the
 composition quantities in the EOS directly from ``eos_state.aux[]``
 instead of from ``eos_state.xn[]``.  The ``AUX_THERMO`` preprocessor
 variable is enabled in this case, and the equations of state interpret

Docs/source/refs.bib

@@ -782,4 +782,33 @@ @article{lsode
         author = {{Radhakrishnan}, Krishnan and {Hindmarsh}, Alan C.},
         journal = {Lawrence Livermore National Laboratory Report UCRL-ID-113855},
         pages = {124}
-}
+}
+
+@article{pynucastro,
+author = {{Willcox}, D.~E. and {Zingale}, M.},
+    title = "{pynucastro: an interface to nuclear reaction rates and code generator for reaction network equations}",
+  journal = {Journal of Open Source Software},
+     year = 2018,
+   volume = 3,
+   number = 23,
+   pages = 588,
+    url = {https://doi.org/10.21105/joss.00588},
+   doi = {10.21105/joss.00588},
+  subject = {nuclear astrophysics}
+}
+
+@article{pynucastro2,
+doi = {10.3847/1538-4357/acbaff},
+url = {https://dx.doi.org/10.3847/1538-4357/acbaff},
+year = {2023},
+month = {apr},
+publisher = {The American Astronomical Society},
+volume = {947},
+number = {2},
+pages = {65},
+author = {Alexander I. Smith and Eric T. Johnson and Zhi Chen and Kiran Eiden and Donald E. Willcox and Brendan Boyd and Lyra Cao and Christopher J. DeGrendele and Michael Zingale},
+title = {pynucastro: A Python Library for Nuclear Astrophysics},
+journal = {The Astrophysical Journal},
+abstract = {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.},
+     subject = {nuclear astrophysics}
+}