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  1. Abstract

    A method for modelling the prompt production of molecular states using the hadronic rescattering framework of the general-purpose Pythia event generator is introduced. Production cross sections of possible exotic hadronic molecules via hadronic rescattering at the LHC are calculated for the$$\chi _{c1}(3872)$$χc1(3872)resonance, a possible tetraquark state, as well as three possible pentaquark states,$$P_c^+(4312)$$Pc+(4312),$$P_c^+(4440)$$Pc+(4440), and$$P_c^+(4457)$$Pc+(4457). For the$$P_c^+$$Pc+states, the expected cross section from$$\Lambda _b$$Λbdecays is compared to the hadronic-rescattering production. The$$\chi _{c1}(3872)$$χc1(3872)cross section is compared to the fiducial$$\chi _{c1}(3872)$$χc1(3872)cross-section measurement by LHCb and found to contribute at a level of$${\mathcal {O}({1\%})}$$O(1%). Finally, the expected yields of$$\mathrm {P_c^{+}}$$Pc+production from hadronic rescattering during Run 3 of LHCb are estimated. The prompt background is found to be significantly larger than the prompt$$\mathrm {P_c^{+}}$$Pc+signal from hadronic rescattering.

     
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  2. Free, publicly-accessible full text available June 16, 2024
  3. First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem. 
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    Free, publicly-accessible full text available April 21, 2024
  4. We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions, when the Lund string model of hadronization implemented in Pythia is restricted to the emissions of pions only. The trained models are then used to generate the full hadronization chains, with an IR cutoff energy imposed externally. The hadron multiplicities and cumulative kinematic distributions are shown to match the Pythia generated ones. We also discuss possible future generalizations of our results. 
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  5. This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision "events", i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/. 
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