Nuclear structure function using b-dependent solution of the Balitsky-Kovchegov equation

In this work we have used the solution of the impact-parameter dependent Balitsky-Kovchegov equation with the collinearly improved kernel to predict nuclear structure function. We have proposed two models. First model is a Glauber-Gribov approach with a scattering amplitude from the solution of the BK equation as an imput. The second is the solution of b-dependent BK equation with initial conditions resembling nuclear target. Here you can find the data sets associated with this work.

If you want to use this data set, please cite the following:
J.Cepila, J.G.Contreras and M.Matas, Predictions for nuclear structure functions from the impact-parameter dependent Balitsky-Kovchegov equation, arXiv:2002.11056.

You can download the computed dat sets and an example in Python here: nuclear_b_ciBK_data.zip.

Contact person: Marek Matas, marek.matas@fjfi.cvut.cz

README.txt:

Hi, welcome to the page with the data files regarding the scattering amplitude computed with the BK equation with the collinearly improved kernel for NUCLEI. The computation was carried out as described in paper 2002.11056. ########################## DATA FORMAT ############################# The scattering amplitude is stored in "ciBK_nuclear_data/b-BK-##_A###/N(r,b,Y).txt", where ### stands for the atomic number of the desired element and ## for either GG (Glauber-Gribov model) or A (BK-A model). The grids, that have been used for r and b are stored in "ciBK_nuclear_data/grid_in_r_and_b.txt" The grid for the values of Y are stored in "data/A###/grid_in_Y.txt" ##### N(r,b,Y) ##### Each line in this file is the scattering amplitude N for all values of the impact parameter b for a fixed r and Y. The next line is again N for all values in b but for the next r and the same rapidity Y. After 276 lines, once the final value of r is reached, the next step in rapidity starts. Formaly, the format can be written as: N(r[0], b[0], Y[0]) N(r[0], b[1], Y[0]) ........... N(r[0], b[275], Y[0]) N(r[1], b[0], Y[0]) N(r[1], b[1], Y[0]) ........... N(r[1], b[275], Y[0]) . . . . N(r[275], b[0], Y[0]) N(r[275], b[1], Y[0]) ........... N(r[275], b[275], Y[0]) N(r[0], b[0], Y[1]) N(r[0], b[1], Y[1]) ........... N(r[0], b[275], Y[1]) N(r[1], b[0], Y[1]) N(r[1], b[1], Y[1]) ........... N(r[1], b[275], Y[1]) . . . . N(r[275], b[0], Y[1]) N(r[275], b[1], Y[1]) ........... N(r[275], b[275], Y[1]) N(r[0], b[0], Y[2]) N(r[0], b[1], Y[2]) ........... N(r[0], b[275], Y[2]) . . . ##### r, b ##### The grid for r and b was chosen to be identical, logarithmic from 10**-7 to 10**4 [1/GeV]. The grid has 276 points in total, making it exactly 25 point per order of magnitude. ##### Y ##### Rapidity is computed in linear steps with the step lenght 0.1. ########################## PLOTTING ############################# All produced plots are saved in "plots/png" and "plots/eps" depending on the format. The plotting macro is called "plot.py" and it is a Python script. After installing packages "numpy" and "matplotlib", you can run it with python plot.py or python3 plot.py You can change the variables deciding which value of r/b to plot and which rapidity at the bottom of the script in . plot_vsb(r_index=175, Y=0.2, save_values_of_N=False, element=40, Glauber=False) plot_vsr(b_index=100, Y=1.1, save_values_of_N=False, element=40, Glauber=False) Further instructions for plotting are discussed in the script.