Data driven extraction of jet energy loss distributions in heavy ion collisions

Code Authors: Long-Gang Pang, Ya-Yun He and Xin-Nian Wang

Introduction

This python package is a simple tool to extract the pt loss distribution and the mean pt loss as a function of jet pt, from the experimental single jet RAA for AA collisions at a specific beam energy (with pt spectra in proton+proton collisions at the same beam energy) or the single hadron/gamma hadron pt spectra (without pt spectra in proton+proton collisions).

Example:

from jeteloss import PythiaPP, RAA2Eloss
pp_x, pp_y = PythiaPP(sqrts_in_gev = 2760)
raa_fname = "RAA_2760.txt"
eloss = RAA2Eloss(raa_fname, pp_x, pp_y)
eloss.train()
eloss.save_results()
eloss.plot_mean_ptloss()
eloss.plot_pt_loss_dist()

The format of input data "RAA_2760.txt": The first row is the comment row start with "#" and data description for the following columns, "RAA_x, RAA_xerr, RAA_y, RAA_yerr" where RAA_x is the pt bins, RAA_xerr is the uncertainties of these pt bins, RAA_y is the RAA value in one A+A collisions, RAA_yerr is the uncertainties of RAA_y.

Results

Citation

If you have used this package to produce results for presentation/publications, please cite the following two papers, from where one can find the detailed information of the underlying physics.

@article{He:2018gks, author = "He, Yayun and Pang, Long-Gang and Wang, Xin-Nian", title = "{Bayesian extraction of jet energy loss distributions in heavy-ion collisions}", year = "2018", eprint = "1808.05310", archivePrefix = "arXiv", primaryClass = "hep-ph", SLACcitation = "%%CITATION = ARXIV:1808.05310;%%" }

@article{He:2018xjv, author = "He, Yayun and Cao, Shanshan and Chen, Wei and Luo, Tan and Pang, Long-Gang and Wang, Xin-Nian", title = "{Conspiracy behind single inclusive jet suppression in heavy-ion collisions}", year = "2018", eprint = "1809.02525", archivePrefix = "arXiv", primaryClass = "nucl-th", SLACcitation = "%%CITATION = ARXIV:1809.02525;%%" }

Installation

Method 1: using pip

Step 1:

pip install jeteloss

Step 2:

git clone [email protected]:lgpang/jeteloss.git

Step 3:

cd jeteloss/examples

python example1.py

Method 2: install from local directory

Step 1: download the code from github

git clone [email protected]:lgpang/jeteloss.git

Step 2: install jeteloss and dependences

cd jeteloss

python setup.py install

Step 3: run example code

cd examples

python example1.py

Method 3: using anaconda

Step 1: To create one clean python virtual environment

conda create -n test_jeteloss python=3.6

Step 2: To activate this environment, use:

source activate test_jeteloss

Step 3: Install jeteloss module and its dependences

pip install jeteloss

Step 4: Run the example code downloaded using:

git clone [email protected]:lgpang/jeteloss.git

cd jeteloss/examples

python example1.py

Step 5: To deactivate an active environment, use:

source deactivate

Step 6: Clean up To see how many environments do you have, use:

conda env list

To remove one environment, use:

conda remove --name test_jeteloss --all