Dataset and scripts for "The curvature of the pseudo-critical line in the QCD phase diagram from mesonic lattice correlation functions"

2024/12/30 by Antonio Smecca, Gert Aarts, Smecca, Antonio +14 · 1 citation
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Physics of Superconductivity and Magnetism #Quantum Chromodynamics and Particle Interactions #Theoretical and Computational Physics

paper · pdf · doi:10.48550/arxiv.2412.20922

openalex publication_date 2024/12/30 · openalex created_date 2025/01/01 · openalex updated_date 2026/07/28

Abstract

General Info: Dataset and Analysis scripts for the study of the QCD pseudocritical line curvature using lattice QCD described in 2412.20922. gen2.zip and gen2l.xip contain raw correlators for produced using the Generation 2 (2L) ensembles produced by the FASTSUM collaboration. Requirements for analysis scripts: - Any version of Python 3 available- The 'gvar' library - https://github.com/gplepage/gvar Analysis scripts: The most complete version of the analysis scripts are on the github repository: https://github.com/asmecca/mu2_curvature.git Instructions: 1. unzip analysis.zip, gen2.zip and gen2l.zip 2. modify 'iter_mu2.sh' in both gen2 and Gen2L directories, inserting the correct path to the directories NtxNs 3. run 'iter_mu2.sh' for both gen2 and Gen2L 4. modify the file 'final_ratio.py' inserting the correct path to the correlators directory NtxNs for both gen2 and Gen2L 5. run 'launch_ratio.sh' for both gen2 and Gen2L 6. run 'launch_interpolate.sh' for both gen2 and Gen2L 7. run 'final_fit_mu.py' to obtain final plot and kappa values 8. run 'plot_interpolate.py' to obtain the plots showing the interpolation of the data as shown in the paper. It takes the value of mu_q as input Attention! - ''launch_interpolate.sh'' appends the values of Tpc to a file, so if it is not the first run you should delete the previous run Other We are grateful to the HadSpec collaboration for the use of their zero temperature ensembles. This work is supported by the UKRI Science and Technology Facilities Council (STFC) Consolidated Grant No. ST/X000648/1. We are grateful to Supercomputing Wales for the use of their computing resources and to the Swansea Academy for Advanced Computing for support. This work used the DiRAC Data Intensive service (DIaL2 / DIaL2.5) at the University of Leicester, managed by the University of Leicester Research Computing Service on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The DiRAC service at Leicester was funded by BEIS, UKRI and STFC capital funding and STFC operations grants. DiRAC is part of the UKRI Digital Research Infrastructure, and the DiRAC Blue Gene Q Shared Petaflop system at the University of Edinburgh, operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BIS National E-infrastructure capital grant ST/K000411/1, STFC capital grant ST/H008845/1, and STFC DiRAC Operations grants ST/K005804/1 and ST/K005790/1. DiRAC is part of the National E-Infrastructure. This work was performed using the PRACE Marconi-KNL resources hosted by CINECA, Italy. We acknowledge EuroHPC Joint Undertaking for awarding the project EHPC-EXT-2023E01-010 access to LUMI-C, Finland. SK is supported by the National Research Foundation of Korea under grant NRF-2021R1A2C1092701 fundedby the Korean government (MEST) and by the Institute of Information & Communication Technology Planning & Evaluation grant funded by the Korean government (Ministry of Science and ICT) (IITP-2024-RS-2024-00437191).

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