Final data for paper "A nonperturbative test of nucleation calculations for strong phase transitions"
<p>In this Zenodo deposit we include the data used to make three key figures in our paper, showing our final nucleation rate results. The columns of the raw data files are labelled. In each case, the quantity being tabulated is the logarithm of the nucleation rate, typically labelled <em>logGamma</em> in our data files. We also include plotting scripts to generate the figures from the data.</p> <ul> <li> <p>The directory <code>a_limit</code> contains the extrapolation to zero lattice spacing, with the raw data in the file <code>a_limit_data</code>. The temperature <em>T</em> is fixed to our benchmark point of 93.121 GeV. The linear size <em>Nx</em> and the lattice spacing <em>a</em> are varied to give a constant total volume. The file <code>a_limit_lin_op_data</code> contains our comparison measurement using the linear order parameter. The script <code>plot_a_limit.py</code> performs a nonlinear least squares fit to a cubic, writes the continuum extrapolated nuclation rate result to stdout, and plots the data and the fit.</p> </li> <li> <p>The directory <code>vol_limit</code> contains the extrapolation to infinite volume, with the raw data in the file <code>vol_limit_data</code>. Here the temperature <em>T</em>=93.121 GeV and lattice spacing <em>a</em>=1.5 are fixed, while the linear size <em>Nx</em> is varied. The script <code>plot_vol_limit.py</code> performs a nonlinear least squares fit to an exponential function, writes the infinite volume limit nucleation rate result to stdout, and again plots the data and the fit.</p> </li> <li> <p>The directory <code>rate_reweighted</code> contains the data needed to plot the nucleation rate as a function of temperature. This plot combines lattice data and various perturbative estimates of the nucleation rate. The lattice data are in three files:</p> <ul> <li><code>rate_lattice_BM2_vol_limit</code> contains the infinite volume limit result at the simulated lattice temperature <em>T</em>=93.121 GeV and with lattice spacing <em>a</em>=1.5.</li> <li><code>rate_lattice_BM2</code> reweights the lattice data to different temperatures and then takes the infinite volume limit.</li> <li><code>rate_lattice_BM2_Nx40_a1.5</code> contains data reweighted from the simulation at fixed volume <em>Nx</em>=40. In each lattice data file, the temperature, continuum potential parameters and nucleation rate are given.</li> </ul> <p>The perturbative results are in three files, <code>rate_perturbative_BM2_rg_low</code>, <code>rate_perturbative_BM2</code> and <code>rate_perturbative_BM2_rg_high</code>, corresponding to the three RG scales mentioned in the text. These files again contain the temperature and continuum lattice parameters. The column <em>eps</em> gives the dimensionless loop expansion parameter around the metastable phase within the EFT. The column <em>logGamma_0</em> gives the tree level result, <em>logGamma_A</em> and <em>logGamma_B</em> the LPA results with the two options for dealing with the imaginary parts described in the text, and <em>logGamma_1</em> is the one loop result. The script <code>plot_rate_reweighted.py</code> plots all of these data together. For the tree level and one loop cases, the error bands plotted correspond to the minimum and maximum values of the nucleation rate across the three RG scales. For the LPA results, the bands include the extreme values for all six options including the two approaches to handling the imaginary parts.</p> </li> </ul> <p>In each case, the plots are saved to PDF and PNG plot files.</p> <p>The figures used in this deposit and in the paper used the following package versions (obtained with the <code>pipreqs</code> package):</p> <pre><code>matplotlib==3.5.1 numpy==1.21.5 scipy==1.8.0 seaborn==0.11.2</code></pre> <p>Note that in <a href="https://doi.org/10.5281/zenodo.10891524">Version v1</a> there was an error in the normalisation of our perturbative tree-level and LPA results which has now been fixed.</p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4
ScienceDex for faculty
Your academic CV and NIH biosketch
Make your CV in your school's format and your NIH biosketch from it, then download both as Word files.