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5 results for “r-process”
Detailed abundances based on different nuclear physics for theoretical r-process scenarios
<p>This data set contains detailed abundances (at a time t=10^6 years after the event) for individual trajectories for seven different simulations of potential r-process sites, and based on nine different combinations of nuclear mass models and fission fragment distribution models. The data have been used and are discussed in Cote, Eichler, Yagüe, et al. (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) to determine the isotopic ratios of I129/Cm247 and compare them to meteoritic data.</p> <p>Furthermore, a code is included which samples a subset of trajectories reproducing the measured meteoritic I129/Cm247 abundance ratio of 438 +- 92. See the README file and the publication (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) for more details.</p>
r-process abundances in neutron-rich merger ejecta given different theoretical nuclear physics inputs
<p>This data release contains nucleosynthesis predictions for the r-process abundances presented in Côté, Eichler, Yagüe, Vassh et al. (2021) for compact object merger ejecta based on the publicly available simulation trajectories of Rosswog et al. (2013). All ejecta for the merger scenarios considered here are very neutron-rich (Ye ~ 0.016-0.11). Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating (here with a reheating efficiency of 50%). Results are reported for several different theoretical nuclear physics inputs but all calculations make use of the GEF fission yield prescription (see Vassh et al. 2019). All abundances are given at 1 Myr (10^6 years) post-merger. Please see the README file for more details and references.</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh et al. 2021), and refer to Vassh et al. (2019) and Côté, Eichler, Yagüe, Vassh et al. (2021) for further details on the nuclear data applied as well as Rosswog et al. (2013), Piran et al. (2013), and Korobkin et al. (2012) for further details on the merger ejecta trajectories.</p>
r-process abundances in neutron star merger dynamical ejecta given different fission yields
<p>This data release contains nucleosynthesis predictions following Vassh et al. (2020) for the r-process abundances of binary neutron star merger ejecta based on the simulation trajectories of Radice et al. (2018), which were mapped to parametrized trajectories based on their neutron-richness (Ye), entropy (s), and expansion timescale (tau). The neutron star merger scenarios considered here cover a wide range of neutron star masses. Calculations were performed with the PRISM code (Mumpower et al. 2018) which accounts for nuclear reheating. Results are reported for two fission yield sets, the Finite Range Liquid Drop Model (FRLDM, Mumpower et al. 2020) and 50/50 symmetric splits. All calculations assumed FRDM12 (Möller et al. 2016) for the nuclear mass model and apply the SFHO equation of state when obtaining the initial composition from nuclear statistical equilibrium (NSE).</p> <p>When using these nucleosynthesis yields, please cite this Zenodo data release (Vassh 2022). Refer to Vassh et al. (2020) for further details on the nuclear physics inputs, to Mumpower et al. (2018) for further details on FRLDM, and to Radice et al. (2018) for further details on the merger ejecta trajectories.</p>
Final abundances used in "Impact of level densities and gamma-strength functions on r-process simulations"
<p>Dataset of final abundances used in the article "Impact of level densities and gamma-strength functions on r-process simulations", found on ArXiv at https://arxiv.org/abs/2305.03664.</p> <p>The five files named "final_y_x_ext_REACLIB" contain the abundances calculated using the REACLIB rates and the x trajectory.</p> <p>The five folders represent the five different trajectories used, and they each contain 48 final abundance files. These were calculated using the 48 different neutron-capture rate combinations from the 6 nuclear level density and the 8 gamma-strength function models available in TALYS 1.95. The names indicate the trajectory, the mass model, the nuclear level density, the gamma-strength function and the optical model potential used. For example, the file named "b_output_m2n1s7o2_noDZ" contains the final abundances obtained using trajectory "b", mass model 2, nuclear level density 1, strength 7 and optical model potential 2 (in this case meaning keyword "localomp n" in TALYS 1.95). For details and references, see the article.</p> <p>The first column in all files indicates the mass number A, the second column the final abundance Y.</p>
A Nuclear Equation of State Inferred from Stellar r-Process Abundances: Data
<p>This repository contains the raw MCMC posteriors as H5 files for the three cases presented in "A Nuclear Equation of State Inferred from Stellar <em>r</em>-Process Abundances" (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p> <p>Also included are Python scripts with a variety of functions to read the H5 files and interpret the data with <a href="https://git.ligo.org/lscsoft/lalsuite">LALSuite</a>. These include:</p> <ul> <li>reading the data contained in the H5 file (likelihood, acceptance, and values for each MCMC step)</li> <li>generating a corner plot of the data</li> <li>finding the maximum likelihood in the posterior distribution</li> <li>calculating a neutron star mass-radius curve for a posterior EOS</li> <li>calculating pressure and density for a posterior EOS</li> <li>calculating observables (M_TOV, R_1.4, and L) associated with an EOS</li> </ul> <p>The scripts are written for Python3 compatibility and depend on:</p> <ul> <li><a href="https://pypi.org/project/lalsuite/">lalsuite</a></li> <li><a href="https://docs.h5py.org/en/stable/">h5py</a></li> <li><a href="https://numpy.org/">numpy</a></li> <li><a href="https://scipy.org/">scipy</a></li> <li><a href="https://matplotlib.org/">matplotlib</a></li> <li><a href="https://corner.readthedocs.io/en/latest/">corner</a></li> </ul> <p>For more information and how to use these scripts, see the comments in `example.py` or contact Erika Holmbeck.</p> <p>If any of our posterior samples are used in your work, we ask that you appropriately cite this repository and the original paper (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211006432H/abstract">Holmbeck et al., arXiv:2110.06432</a>).</p>
ScienceDex guides
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