Model consistent sensisivity study of the r-process using SkyNet
In this, I use the SkyNet package from J. Lippuner (https://jonaslippuner.com/research/skynet/) in order to simulate the r-process with model-consistent nuclear input. The nuclear reaction code TALYS (1.95) includes 6 different nuclear level densities (NLDs) and 8 different gamma-strength functions (GSFs), and combining these we can obtain 48 different neutron capture rates, which are the nuclear input for network nucleosynthesis calculations. This is done by fitting the TALYS solutions to the NON-SMOKER formula, and replacing the REACLIB coefficients with the ones from the fittings. The new REACLIB files are then read by SkyNet, which runs a r-process simulation using an astrophysical trajectory from Arcones 2007 (https://doi.org/10.1051/0004-6361:20066983). SkyNet reproduces the results obtained by Mumpower et al. (2016, https://doi.org/10.1016/j.ppnp.2015.09.001), and thus we can compare the results we get from model consistent nuclear input to the the uncertainties obtained with the Monte Carlo simulations from Mumpower et al. (2016).
In order to run the codes, one needs a dataset with all TALYS n-capture calculations for all model combinations, possibly tabulated as they take a lot of space. I might upload these sometimes, but if you need them, please take contact. Once the dataset is obtained, one can run make_reaclibs.py in order to translate the TALYS files into REACLIB coefficients, so that SkyNet is able to read them. This might take some time (5h on my machine), but the code can proably parallelized. Once the REACLIB files are made, each with their distinctive suffix, they might be moved to the "reaclibs" folder, and the script r-process_b_parallell.py can be run. This is already parallelized, and might take ~9h to run. When the results are ready, they might be plotted with the plot_abundances.py code.