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Global tuning of hadronic interaction models with accelerator-based and astroparticle data

Abstract

In high-energy and astroparticle physics, event generators have an essential role, even in the simplest data analyses. Physical processes occurring in hadronic collisions are simulated within a Monte Carlo framework but a major challenge remains modelling of hadron dynamics at low momentum transfer, which includes the initial and final phases of every hadronic collision. Phenomenological models inspired by quantum chromodynamics used for these phases cannot guarantee completeness or correctness over the full phase space. These models usually include parameters which must be tuned to suitable experimental data. Until now, event generators have primarily been developed and tuned based on data from high-energy physics experiments at accelerators. However, in many cases, they have been found to not satisfactorily describe data from astroparticle experiments, which provide sensitivity especially to hadrons produced nearly parallel to the collision axis and cover centre-of-mass energies up to several hundred tera-electronvolts, well beyond those reached at colliders so far. Here, we address the complementarity of these two sets of data and present a roadmap for a unified tuning of event generators with accelerator-based and astroparticle data.

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Fig. 1: Predictions from hadronic interaction models for different interaction scenarios in comparison with data.
Fig. 2: Effects of changing basic observables of hadronic interactions to different extensive air shower observables for 1019.5 eV proton showers simulated with CONEX using SIBYLL 2.1 as the baseline model.
Fig. 3: Muon content of extensive air showers as a function of the shower energy E from different experiments.
Fig. 4: Schematic overview of tuning of event generators in high-energy physics and the extension to include data from astroparticle physics.

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References

  1. Navas, S. et al. Review of particle physics. Phys. Rev. D 110, 030001 (2024).

    Article  ADS  Google Scholar 

  2. Barone, G. et al. Higgs production via vector–boson fusion at the LHC. SciPost Phys. Comm. Rep. https://doi.org/10.21468/SciPostPhysCommRep.13 (2025).

  3. Blumer, J., Engel, R. & Horandel, J. R. Cosmic rays from the knee to the highest energies. Prog. Part. Nucl. Phys. 63, 293–338 (2009).

    Article  ADS  Google Scholar 

  4. Kampert, K.-H. & Unger, M. Measurements of the cosmic ray composition with air shower experiments. Astropart. Phys. 35, 660–678 (2012).

    Article  ADS  Google Scholar 

  5. The Pierre Auger Collaboration. The Pierre Auger Cosmic Ray Observatory. Nucl. Instrum. Methods Phys. Res. A 798, 172–213 (2015).

    Article  ADS  Google Scholar 

  6. Bastieri, D. et al. Using the photons from the Crab Nebula seen by GLAST to calibrate MAGIC and the imaging air Cerenkov telescopes. Astropart. Phys. 23, 572–576 (2005).

    Article  ADS  Google Scholar 

  7. Meyer, M., Horns, D. & Zechlin, H. S. The Crab Nebula as a standard candle in very high-energy astrophysics. Astron. Astrophys. 523, A2 (2010).

    Article  ADS  Google Scholar 

  8. Albrecht, J. et al. The muon puzzle in cosmic-ray induced air showers and its connection to the Large Hadron Collider. Astrophys. Space Sci. 367, 27 (2022).

    Article  ADS  Google Scholar 

  9. Sterman, G. et al. Handbook of perturbative QCD. Rev. Mod. Phys. 67, 157–248 (1995).

    Article  ADS  Google Scholar 

  10. Kalaydzhyan, T. & Shuryak, E. Collective flow in high-multiplicity proton–proton collisions. Phys. Rev. C 91, 054913 (2015).

    Article  ADS  Google Scholar 

  11. Adam, J. et al. Enhanced production of multi-strange hadrons in high-multiplicity proton–proton collisions. Nat. Phys. 13, 535–539 (2017).

    Article  Google Scholar 

  12. Baur, S. et al. Combined analysis of accelerator and ultra-high energy cosmic ray data. PoS ICRC2015, 418 (2016).

    Google Scholar 

  13. Petrov, V. A., Ryutin, R. A. & Sobol, A. E. LHC as pi p and pi pi collider. Eur. Phys. J. C 65, 637–647 (2010).

    Article  ADS  Google Scholar 

  14. Ryutin, R. A., Petrov, V. A. & Sobol, A. E. Towards extraction of π+p and π+π+ cross-sections from charge exchange processes at the LHC. Eur. Phys. J. C 71, 1667 (2011).

    Article  ADS  Google Scholar 

  15. Maurin, D. et al. Precision cross-sections for advancing cosmic-ray physics and other applications: a comprehensive programme for the next decade. Phys. Rep. 1161, 1–81 (2026).

    Article  ADS  Google Scholar 

  16. Bass, S. A. et al. Microscopic models for ultrarelativistic heavy ion collisions. Prog. Part. Nucl. Phys. 41, 255–369 (1998).

    Article  ADS  Google Scholar 

  17. Bierlich, C. et al. A comprehensive guide to the physics and usage of PYTHIA 8.3. SciPost Phys. Codeb. 2022, 8 (2022).

    Article  Google Scholar 

  18. Maris, I. C. et al. Influence of low energy hadronic interactions on air-shower simulations. Nucl. Phys. B Proc. Suppl. 196, 86–89 (2009).

    Article  ADS  Google Scholar 

  19. Sjöstrand, T. & Utheim, M. Hadron interactions for arbitrary energies and species, with applications to cosmic rays. Eur. Phys. J. C 82, 21 (2022).

    Article  ADS  Google Scholar 

  20. Nilsson-Almqvist, B. & Stenlund, E. Interactions between hadrons and nuclei: the Lund Monte Carlo, Fritiof Version 1.6. Comput. Phys. Commun. 43, 387 (1987).

    Article  ADS  Google Scholar 

  21. Agostinelli, S. et al. GEANT4 — a simulation toolkit. Nucl. Instrum. Meth. A 506, 250–303 (2003).

    Article  ADS  Google Scholar 

  22. Gribov, L. V., Levin, E. M. & Ryskin, M. G. Semihard processes in QCD. Phys. Rept. 100, 1–150 (1983).

    Article  ADS  Google Scholar 

  23. Drescher, H. J., Hladik, M., Ostapchenko, S., Pierog, T. & Werner, K. Parton based Gribov–Regge theory. Phys. Rept. 350, 93–289 (2001).

    Article  ADS  Google Scholar 

  24. Good, M. L. & Walker, W. D. Diffraction dissociation of beam particles. Phys. Rev. 120, 1857–1860 (1960).

    Article  ADS  Google Scholar 

  25. Riehn, F., Engel, R., Fedynitch, A., Gaisser, T. K. & Stanev, T. Hadronic interaction model SIBYLL 2.3d and extensive air showers. Phys. Rev. D 102, 063002 (2020).

    Article  ADS  Google Scholar 

  26. Werner, K. Revealing a deep connection between factorization and saturation: new insight into modeling high-energy proton–proton and nucleus–nucleus scattering in the EPOS4 framework. Phys. Rev. C 108, 064903 (2023).

    Article  ADS  Google Scholar 

  27. Pierog, T. & Werner, K. EPOS LHC-R: up-to-date hadronic model for EAS simulations. PoS ICRC2023, 230 (2023).

    Google Scholar 

  28. Ostapchenko, S. QGSJET-III model of high energy hadronic interactions: II. Particle production and extensive air shower characteristics. Phys. Rev. D 109, 094019 (2024).

    Article  ADS  Google Scholar 

  29. Glauber, R. J. & Matthiae, G. High-energy scattering of protons by nuclei. Nucl. Phys. B 21, 135–157 (1970).

    Article  ADS  Google Scholar 

  30. Engel, J., Gaisser, T. K., Stanev, T. & Lipari, P. Nucleus–nucleus collisions and interpretation of cosmic ray cascades. Phys. Rev. D 46, 5013–5025 (1992).

    Article  ADS  Google Scholar 

  31. Bierlich, C., Gustafson, G., Lönnblad, L. & Shah, H. The Angantyr model for heavy-ion collisions in PYTHIA8. J. High Energy Phys. 10, 134 (2018).

    Article  ADS  Google Scholar 

  32. Reininghaus, M. Air showers and hadronic interactions with CORSIKA 8. SciPost Phys. Proc. 15, 019 (2024).

    Article  Google Scholar 

  33. Eskola, K. J., Paukkunen, H. & Salgado, C. A. EPS09: a new generation of NLO and LO nuclear parton distribution functions. J. High Energy Phys. 04, 065 (2009).

    Article  ADS  Google Scholar 

  34. Eskola, K. J., Paakkinen, P., Paukkunen, H. & Salgado, C. A. EPPS16: nuclear parton distributions with LHC data. Eur. Phys. J. C 77, 163 (2017).

    Article  ADS  Google Scholar 

  35. Andersson, B., Gustafson, G., Ingelman, G. & Sjostrand, T. Parton fragmentation and string dynamics. Phys. Rep. 97, 31–145 (1983).

    Article  ADS  Google Scholar 

  36. Christiansen, J. R. & Skands, P. Z. String formation beyond leading colour. J. High Energy Phys. 08, 003 (2015).

    Article  ADS  Google Scholar 

  37. Bierlich, C., Gustafson, G., Lönnblad, L. & Tarasov, A. Effects of overlapping strings in pp collisions. J. High Energy Phys. 03, 148 (2015).

    Article  Google Scholar 

  38. Bierlich, C., Gustafson, G. & Lönnblad, L. Collectivity without plasma in hadronic collisions. Phys. Lett. B. 779, 58–63 (2018).

    Article  ADS  Google Scholar 

  39. Ilten, P. & Utheim, M. Forming molecular states with hadronic rescattering. Eur. Phys. J. A 58, 1 (2022).

    Article  ADS  Google Scholar 

  40. Wang, X.-N. & Gyulassy, M. HIJING: a Monte Carlo model for multiple jet production in pp, pA and AA collisions. Phys. Rev. D 44, 3501–3516 (1991).

    Article  ADS  Google Scholar 

  41. Deng, W.-T., Wang, X.-N. & Xu, R. Hadron production in p+p, p+Pb, and Pb+Pb collisions with the HIJING 2.0 model at energies available at the CERN Large Hadron Collider. Phys. Rev. C 83, 014915 (2011).

    Article  ADS  Google Scholar 

  42. Kalmykov, N. N., Khristiansen, G. B., Ostapchenko, S. S. & Pavlov, A. I. The predictions of quark–gluon string model and the data of air showers at ultra high energies. Int. Cosmic Ray Conf. 1, 123 (1995).

    Google Scholar 

  43. Pyras, L., Glaser, C., Hallmann, S. & Nelles, A. Atmospheric muons at PeV energies in radio neutrino detectors. J. Cosmol. Astropart. Phys. 10, 043 (2023).

    Article  ADS  Google Scholar 

  44. Ulrich, R., Pierog, T. & Baus, C. Cosmic Ray Monte Carlo Package, CRMC (2021).

  45. Dembinski, H., Fedynitch, A. & Prosekin, A. Chromo: an event generator frontend for particle and astroparticle physics. PoS ICRC2023, 189 (2023).

    Google Scholar 

  46. Aartsen, M. G. et al. The IceCube Neutrino Observatory: instrumentation and online systems. J. Instrum. 12, P03012 (2017).

    Google Scholar 

  47. Koehne, J. H. et al. PROPOSAL: a tool for propagation of charged leptons. Comput. Phys. Commun. 184, 2070–2090 (2013).

    Article  ADS  Google Scholar 

  48. Ferrari, A., Sala, P. R., Fasso, A. & Ranft, J. FLUKA: a multi-particle transport code (Program Version 2005) (2005).

  49. Abreu, P. et al. Techniques for measuring aerosol attenuation using the Central Laser Facility at the Pierre Auger Observatory. J. Instrum. 8, P04009 (2013).

    Google Scholar 

  50. Shibata, T. et al. Absolute energy calibration of the telescope array fluorescence detector with an electron linear accelerator. EPJ Web Conf. 53, 10004 (2013).

    Article  Google Scholar 

  51. Keilhauer, B. The Balloon-the-Shower programme of the Pierre Auger Observatory. Astrophys. Space Sci. Trans. 6, 27–30 (2010).

    Article  ADS  Google Scholar 

  52. Abreu, P. et al. Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS). Astropart. Phys. 35, 591–607 (2012).

    Article  ADS  Google Scholar 

  53. Heck, D., Knapp, J., Capdevielle, J. N., Schatz, G. & Thouw, T. CORSIKA: a Monte Carlo code to simulate extensive air showers. https://doi.org/10.5445/IR/270043064 (1998).

  54. Sciutto, S. J. AIRES: a system for air shower simulations. Preprint at https://arxiv.org/abs/astro-ph/9911331 (1999).

  55. Alves Batista, R. et al. CRPropa 3.2 — an advanced framework for high-energy particle propagation in extragalactic and galactic spaces. J. Cosmol. Astropart. Phys. 09, 035 (2022).

    Google Scholar 

  56. Hillas, A. M. Shower simulation: lessons from MOCCA. Nucl. Phys. B Proc. Suppl. 52, 29–42 (1997).

    Article  ADS  Google Scholar 

  57. Bergmann, T. et al. One-dimensional hybrid approach to extensive air shower simulation. Astropart. Phys. 26, 420–432 (2007).

    Article  ADS  Google Scholar 

  58. Kozynets, T., Fedynitch, A. & Koskinen, D. J. Atmospheric lepton fluxes via two-dimensional matrix cascade equations. Phys. Rev. D 108, 103040 (2023).

    Article  ADS  Google Scholar 

  59. Gaisser, T. K., Engel, R. & Resconi, E. Cosmic Rays and Particle Physics 2nd edn (Cambridge Univ. Press, 2016).

  60. Ortiz, J. A., Medina Tanco, G. A. & de Souza, V. Longitudinal development of extensive air showers: hybrid code SENECA and full Monte Carlo. Astropart. Phys. 23, 463–476 (2005).

    Article  ADS  Google Scholar 

  61. Ulrich, R., Engel, R. & Unger, M. Hadronic multiparticle production at ultra-high energies and extensive air showers. Phys. Rev. D 83, 054026 (2011).

    Article  ADS  Google Scholar 

  62. Baur, S. et al. Core–corona effect in hadron collisions and muon production in air showers. Phys. Rev. D 107, 094031 (2023).

    Article  ADS  Google Scholar 

  63. Bierlich, C. et al. Reweighting Monte Carlo predictions and automated fragmentation variations in Pythia 8. SciPost Phys. 16, 134 (2024).

    Article  ADS  Google Scholar 

  64. Pierog, T., Karpenko, I., Katzy, J. M., Yatsenko, E. & Werner, K. EPOS LHC: test of collective hadronization with data measured at the CERN Large Hadron Collider. Phys. Rev. C 92, 034906 (2015).

    Article  ADS  Google Scholar 

  65. Bailey, S., Cridge, T., Harland-Lang, L. A., Martin, A. D. & Thorne, R. S. Parton distributions from LHC, HERA, Tevatron and fixed target data: MSHT20 PDFs. Eur. Phys. J. C 81, 341 (2021).

    Article  ADS  Google Scholar 

  66. Abdul Khalek, R. et al. Science requirements and detector concepts for the electron-ion collider: EIC Yellow Report. Nucl. Phys. A 1026, 122447 (2022).

    Article  Google Scholar 

  67. Benedikt, M. et al. Future Circular Collider Feasibility Study Report: Volume 1, Physics, Experiments, Detectors, Technical Report. https://cds.cern.ch/record/2928193 (CERN, 2025).

  68. Abgrall, N. et al. NA61/SHINE facility at the CERN SPS: beams and detector system. J. Instrum. 9, P06005 (2014).

    Google Scholar 

  69. Aad, G. et al. The ATLAS experiment at the CERN Large Hadron Collider. J. Instrum. 3, S08003 (2008).

    Google Scholar 

  70. Chatrchyan, S. et al. The CMS experiment at the CERN LHC. J. Instrum. 3, S08004 (2008).

    Google Scholar 

  71. Aamodt, K. et al. The ALICE experiment at the CERN LHC. J. Instrum. 3, S08002 (2008).

    Google Scholar 

  72. Alves, A. A. Jr et al. The LHCb detector at the LHC. J. Instrum. 3, S08005 (2008).

    Google Scholar 

  73. The LHCb Collaboration. Precision luminosity measurements at LHCb. J. Instrum. 9, P12005 (2014).

    Article  Google Scholar 

  74. LHCb SMOG Upgrade, Technical Report. https://cds.cern.ch/record/2673690 (CERN, 2019).

  75. Anelli, G. et al. The TOTEM experiment at the CERN Large Hadron Collider. J. Instrum. 3, S08007 (2008).

    Google Scholar 

  76. Chatrchyan, S. et al. Measurement of pseudorapidity distributions of charged particles in proton–proton collisions at \(\sqrt{s}\) = 8 TeV by the CMS and TOTEM experiments. Eur. Phys. J. C 74, 3053 (2014).

    Article  ADS  Google Scholar 

  77. Sirunyan, A. M. et al. Measurement of the average very forward energy as a function of the track multiplicity at central pseudorapidities in proton–proton collisions at \(\sqrt{s}=13\,{\rm{TeV}}\). Eur. Phys. J. C 79, 893 (2019).

    Article  ADS  Google Scholar 

  78. Adriani, O. et al. The LHCf detector at the CERN Large Hadron Collider. J. Instrum. 3, S08006 (2008).

    Google Scholar 

  79. Abreu, H. et al. The FASER detector. J. Instrum. 19, P05066 (2024).

    Article  Google Scholar 

  80. Adriani, O. et al. Measurement of forward photon production cross-section in proton–proton collisions at \(\sqrt{s}\) = 13 TeV with the LHCf detector. Phys. Lett. B 780, 233–239 (2018).

    Article  ADS  Google Scholar 

  81. Adriani, O. et al. Measurement of very forward neutron energy spectra for 7 TeV proton–proton collisions at the Large Hadron Collider. Phys. Lett. B 750, 360–366 (2015).

    Article  ADS  Google Scholar 

  82. Adriani, O. et al. Measurements of longitudinal and transverse momentum distributions for neutral pions in the forward-rapidity region with the LHCf detector. Phys. Rev. D 94, 032007 (2016).

    Article  ADS  Google Scholar 

  83. Mammen Abraham, R. et al. First measurement of the muon neutrino interaction cross section and flux as a function of energy at the LHC with FASER. Phys. Rev. Lett. 134, 211801 (2025).

    Article  ADS  Google Scholar 

  84. CERN. For one day only LHC collides xenon beams. https://home.cern/news/news/accelerators/one-day-only-lhc-collides-xenon-beams (2017).

  85. CERN. First-ever collisions of oxygen at the LHC. https://home.web.cern.ch/news/news/accelerators/first-ever-collisions-oxygen-lhc (2025).

  86. Brewer, J., Mazeliauskas, A. & van der Schee, W. Opportunities of OO and pO collisions at the LHC. In Opportunities of OO and pO collisions at the LHC. Preprint at https://arxiv.org/abs/2103.01939 (2021).

  87. Anchordoqui, L. A. et al. The forward physics facility: sites, experiments, and physics potential. Phys. Rep. 968, 1–50 (2022).

    Article  ADS  MathSciNet  Google Scholar 

  88. Antoni, T. et al. Electron, muon, and hadron lateral distributions measured in air-showers by the KASCADE experiment. Astropart. Phys. 14, 245–260 (2001).

    Article  ADS  Google Scholar 

  89. Cazon, L., Vazquez, R. A., Watson, A. A. & Zas, E. Time structure of muonic showers. Astropart. Phys. 21, 71–86 (2004).

    Article  ADS  Google Scholar 

  90. Abreu, P. et al. Measurement of the proton–air cross-section at \(\sqrt{s}=57\) TeV with the Pierre Auger Observatory. Phys. Rev. Lett. 109, 062002 (2012).

    Article  ADS  Google Scholar 

  91. Aab, A. et al. Muons in air showers at the Pierre Auger Observatory: mean number in highly inclined events. Phys. Rev. D 91, 032003 (2015).

    Article  ADS  Google Scholar 

  92. Aab, A. et al. Measurement of the fluctuations in the number of muons in extensive air showers with the Pierre Auger Observatory. Phys. Rev. Lett. 126, 152002 (2021).

    Article  ADS  Google Scholar 

  93. Abbasi, R. et al. Improved characterization of the astrophysical muon–neutrino flux with 9.5 years of IceCube Data. Astrophys. J. 928, 50 (2022).

    Article  ADS  Google Scholar 

  94. Dembinski, H. P. et al. Report on tests and measurements of hadronic interaction properties with air showers. EPJ Web Conf. 210, 02004 (2019).

    Article  Google Scholar 

  95. Cazon, L. Working group report on the combined analysis of muon density measurements from eight air shower experiments. PoS ICRC2019, 214 (2020).

    Google Scholar 

  96. Soldin, D. Update on the combined analysis of muon measurements from nine air shower experiments. PoS ICRC2021, 349 (2021).

    Google Scholar 

  97. Arteaga Velazquez, J. C. A report by the WHISP working group on the combined analysis of muon data at cosmic-ray energies above 1 PeV. PoS ICRC2023, 466 (2023).

    Google Scholar 

  98. Aab, A. et al. Muons in air showers at the Pierre Auger Observatory: measurement of atmospheric production depth. Phys. Rev. D 90, 012012 (2014).

    Article  ADS  Google Scholar 

  99. Ostapchenko, S. & Sigl, G. On the model uncertainties for the predicted muon content of extensive air showers. Astropart. Phys. 163, 103004 (2024).

    Article  Google Scholar 

  100. Riehn, F., Engel, R. & Fedynitch, A. SIBYLL: ad-hoc modifications for an improved description of muon data in extensive air showers. PoS ICRC2023, 429 (2023).

    Google Scholar 

  101. Manshanden, J., Sigl, G. & Garzelli, M. V. Modeling strangeness enhancements to resolve the muon excess in cosmic ray extensive air shower data. J. Cosmol. Astropart. Phys. 02, 017 (2023).

    Article  ADS  Google Scholar 

  102. Anchordoqui, L. A., Goldberg, H. & Weiler, T. J. Strange fireball as an explanation of the muon excess in Auger data. Phys. Rev. D 95, 063005 (2017).

    Article  ADS  Google Scholar 

  103. Maguire, E., Heinrich, L. & Watt, G. HEPData: a repository for high energy physics data. J. Phys. Conf. Ser. 898, 102006 (2017).

    Article  Google Scholar 

  104. Maurin, D. et al. A cosmic-ray database update: CRDB v4.1. Eur. Phys. J. C 83, 971 (2023).

    Article  ADS  Google Scholar 

  105. IceCube Collaboration. IceCube Data Release. https://icecube.wisc.edu/science/data-releases/ (2025).

  106. Haungs, A. et al. The KASCADE Cosmic-ray Data Centre KCDC: granting open access to astroparticle physics research data. Eur. Phys. J. C 78, 741 (2018).

    Article  ADS  Google Scholar 

  107. Abdul Halim, A. et al. The Pierre Auger Observatory open data. Eur. Phys. J. C 85, 70 (2025).

    Article  ADS  Google Scholar 

  108. Ostapchenko, S. QGSJET-III model of high energy hadronic interactions: the formalism. Phys. Rev. D 109, 034002 (2024).

    Article  ADS  Google Scholar 

  109. Bierlich, C. et al. Robust independent validation of experiment and theory: RIVET version 3. SciPost Phys. 8, 026 (2020).

    Article  ADS  Google Scholar 

  110. Dobbs, M. & Hansen, J. B. The HepMC C++ Monte Carlo event record for high energy physics. Comput. Phys. Commun. 134, 41–46 (2001).

    Article  ADS  Google Scholar 

  111. Skands, P., Carrazza, S. & Rojo, J. Tuning PYTHIA 8.1: the Monash 2013 Tune. Eur. Phys. J. C 74, 3024 (2014).

    Article  ADS  Google Scholar 

  112. Karneyeu, A., Mijovic, L., Prestel, S. & Skands, P. Z. MCPLOTS: a particle physics resource based on volunteer computing. Eur. Phys. J. C 74, 2714 (2014).

    Article  ADS  Google Scholar 

  113. Korneeva, N., Karneyeu, A. & Skands, P. Event-generator validation with MCPLOTS and LHC@home. Eur. Phys. J. Plus 139, 653 (2024).

    Article  ADS  Google Scholar 

  114. Lhc@home. https://lhcathome.cern.ch/lhcathome/index.php (2025).

  115. Acampora, G. et al. SND@LHC: the scattering and neutrino detector at the LHC. J. Instrum. 19, P05067 (2024).

    Article  Google Scholar 

  116. Fieg, M., Kling, F., Schulz, H. & Sjöstrand, T. Tuning Pythia for forward physics experiments. Phys. Rev. D 109, 016010 (2024).

    Article  ADS  Google Scholar 

  117. Krishnamoorthy, M. et al. Apprentice for event generator tuning. EPJ Web Conf. 251, 03060 (2021).

    Article  Google Scholar 

  118. Gaudu, C. Pythia 8 and air shower simulations: a tuning perspective. In 22nd International Symposium on Very High Energy Cosmic Ray Interactions. Preprint at https://arxiv.org/abs/2411.00111 (2024).

  119. Windau, M., Gaudu, C., Kampert, K.-H. & Kröninger, K. Improving air shower simulations by tuning Pythia 8/Angantyr with accelerator data. PoS ICRC2025, 438 (2025).

    Google Scholar 

  120. Engel, R. et al. Towards a next generation of CORSIKA: a framework for the simulation of particle cascades in astroparticle physics. Comput. Softw. Big Sci. 3, 2 (2019).

    Article  ADS  Google Scholar 

  121. La Cagnina, S., Kröninger, K., Kluth, S. & Verbytskyi, A. A Bayesian tune of the Herwig Monte Carlo event generator. J. Instrum. 18, P10033 (2023).

    Article  Google Scholar 

  122. Schulz, O. et al. BAT.jl: a Julia-based tool for Bayesian inference. SN Comput. Sci. 2, 1–17 (2021).

    Article  Google Scholar 

  123. Dembinski, H. P. et al. Data-driven model of the cosmic-ray flux and mass composition from 10 GeV to 1011 GeV. PoS ICRC2017, 533 (2018).

    Google Scholar 

  124. Pierog, T. & Werner, K. EPOS.LHC-R: a global approach to solve the muon puzzle. PoS ICRC2025, 358 (2025).

    Google Scholar 

  125. Adhikary, H. et al. Measurement of hadron production in π–C interactions at 158 and 350 GeV/c with NA61/SHINE at the CERN SPS. Phys. Rev. D 107, 062004 (2023).

    Article  ADS  Google Scholar 

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Acknowledgements

This paper is a comprehensive overview of work that has been advanced with a collaboration of experts during the workshop ‘Tuning of hadronic interaction models’ (https://indico.uni-wuppertal.de/event/284/) at the Bergische Universität Wuppertal in January 2024. The international workshop was organized as part of the Collaborative Research Center SFB1491, Cosmic Interacting Matters — From Source to Signal. The authors acknowledge the support of the workshop and the related research by many of the workshop participants and authors of this paper by SFB1491, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project no. 445052434. J.A. acknowledges additional support from the Heisenberg Programme of DFG (German Research Foundation) under project no. AL 1639/5-1 and from the Bundesministerium für Bildung und Forschung (BMBF, Federal Ministry of Education and Research) under grant no. 05H21PECL1 within ErUM-FSP T04. H.D. acknowledges funding from the DFG under project no. 449728698. K.-H.K. acknowledges additional support from the BMBF under grant nos. 05A20PX1 and 05A23PX1 and from DFG under project no. 445990517. G.S. acknowledges support from the DFG under Germany’s Excellence Strategy — EXC 2121 Quantum Universe — 390833306 and from the BMBF under grants 05A20GU2 and 05A23GU3. N.K. acknowledges support from the Monash Warwick Alliance as part of the Monash Warwick Alliance in Particle Physics and from the LHC Physics Centre at CERN (LPCC). S.O. acknowledges support from the DFG under project nos. 465275045 and 550225003. F.R. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101065027. T.S. has been supported by the Swedish Research Council under contract no. 2016-05996. L.C. thanks Ministerio de Ciencia e Innovacion/Agencia Estatal de Investigacon (PID2022-140510NB-I00 and RYC2019-027017-I), Xunta de Galicia (CIGUS Network of Research Centers, Consolidation 2021GRCGI-2033, ED431C-2021/22 and ED431F-2022/15) and the European Union (ERDF). J. Blazek, J. Ebr and J.V. have received funding from the following grants: CAS LQ100102401, GACR 21-02226M and MEYS CZ.02.01.01/00/22_008/0004632. P. Paakkinen acknowledges support from the Research Council of Finland (projects 330448 and 331545) and as a part of the Center of Excellence in Quark Matter of the Research Council of Finland (project 364194). R.C. acknowledges support from the Fundação para a Ciência e a Tecnologia (FCT), Portugal, under project https://doi.org/10.54499/2024.06879.CERN. The work of M.V.G. has been supported in part by the DFG through the Research Unit FOR 2926 ‘Next generation pQCD for hadron structure: preparing for the EIC’, project no. 40824754. A.F. and A.P. acknowledge support from Academia Sinica (grant no. AS-GCS-113-M04).

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All authors have contributed to the scientific content and writing of the manuscript. Coordination during the writing process and revisions was done by J.A., H.D., C.G., K.-H.K. and F.R.

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Albrecht, J., Becker Tjus, J., Behling, N. et al. Global tuning of hadronic interaction models with accelerator-based and astroparticle data. Nat Rev Phys 8, 98–114 (2026). https://doi.org/10.1038/s42254-025-00897-3

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