This package is designed to read grids of stellar evolution models from different evolutionary codes and perform interpolation and Bayesian inference against observed systems.
The following code block shows an example of loading a grid with three variable parameters
- masses: sampled logarithmically
$\log M/M_{\odot}=0.9-2.1$ in steps of$0.025$ - rotation:
$\omega/\omega_{crit}=0.0-0.9$ sampled linearly in steps of$0.1$ - overshoot: step overshooting parameter
$\alpha_{overshoot}/0.335=0.5-4.5$ in steps of$0.5$
The function path_constructor defines the location of each simulation based on the input parameters.
using StarStats
using Printf
function path_constructor(strings::Vector{String})
DATA_FOLDER = ENV["STARSTATS_TEST_DATA_FOLDER"]
return DATA_FOLDER*"/LMC/LMC_$(strings[1])_$(strings[2])_$(strings[3]).track.gz"
end
masses = [@sprintf("%.3f", x) for x in range(0.9,2.1,step=0.025)]
rotation = [@sprintf("%.2f", x) for x in range(0.0,0.9,step=0.1)]
overshoot = [@sprintf("%.2f
", x) for x in range(0.5,4.5,step=0.5)]
star_grid = ModelDataGrid([rotation,masses,overshoot],[:rotation,:logM,:overshoot])
load_grid(star_grid,path_constructor,gz_dataframe_loader_with_Teff_and_star_age_fix);
compute_distances_and_EEPs(grid)After loading the grid one can perform interpolations to produce a grid at arbitrary input values. See example below
using LaTeXStrings
using Plots
plot(legend=false,
xflip=true,
xlabel=L"log (T$_{eff}$/K)",
ylabel=L"log (L/L$_{\odot}$)")
xvals = LinRange(0,5, 1000)
rotation = 0.13
logM = 1.21
overshoot = 1.05
logTeff = interpolate_grid_quantity.(Ref(grid),Ref([rotation,logM,overshoot]),:logTeff, xvals)
logL = interpolate_grid_quantity.(Ref(grid),Ref([0.13,1.21,1.05]),:logL, xvals)
plot!(logTeff, logL)
savefig("HR.png")
Using the loaded grid one can perform Bayesian inference of initial parameters of an observed star.
We use the Turing package to perform an MCMC for a given observed values of a star. Below we construct the model that receives values for effective temperature, luminosity and rotation with their corresponding errors.
using Turing, Distributions
@model function star_model(logTeff_obs, logTeff_err, logL_obs, logL_err, vrot_obs, vrot_err, star_grid)
x ~ Uniform(0,3)
logM ~ Uniform(0.9, 1.5)
rotation ~ Uniform(0,0.9)
overshoot ~ Uniform(0.5,1.5)
logTeff = interpolate_grid_quantity(star_grid,[rotation, logM, overshoot],:logTeff,x)
logL = interpolate_grid_quantity(star_grid,[rotation, logM, overshoot],:logL,x)
vrot = interpolate_grid_quantity(star_grid,[rotation, logM, overshoot],:vrot,x)
logTeff_obs ~ Normal(logTeff, logTeff_err)
logL_obs ~ Normal(logL, logL_err)
vrot_obs ~ Normal(vrot, vrot_err)
return logTeff_obs, logL_obs, vrot_obs
endWith this model so defined we run four independent MCMC chains using the NUTS algorithm.
using Logging
Logging.disable_logging(Logging.Warn)
## Here needs more analysis for optimization
num_chains=4
observed_star_model = star_model(4.51974, 0.2, 4.289877, 0.2, 70.7195, 20, star_grid)
star_chains = mapreduce(c -> sample(observed_star_model, NUTS(500,0.9), 20000;stream=false, progress=true), chainscat, 1:num_chains)One can construct the corner plot and obtain the credible intervals for the initial parameters using the get_star_corner_plot function.
using Makie
figure = get_star_corner_plot(star_grid,star_chains)
save("corner_plot.png",figure)```