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Register a new userForward Hadron Productions in high energy pp collisions from a Monte-Carlo generator for Color Glass Condensate
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We develop a Monte-Carlo event generator based on combination of a parton production formula including the effects of parton saturation (called the DHJ formula) and hadronization process due to the Lund string fragmentation model. This event generator is designed for the description of hadron productions at forward rapidities and in a wide transverse momentum range in high-energy proton-proton collisions. We analyze transverse momentum spectra of charged hadrons as well as identified particles; pion, kaon, (anti-)proton at RHIC energy, and ultra-forward neutral pion spectra from LHCf experiment. We compare our results to those obtained in other models based on parton-hadron duality and fragmentation functions.
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We report on a first NLO computation of photon production in p+A collisions at collider energies within the Color Glass Condensate framework, significantly extending previous LO results. At central rapidites, our result is the dominant contribution and probes multi-gluon correlators in nuclei. At high photon momenta, the result is directly sensitive to the nuclear gluon distribution. The NLO result contains two processes, the annihilation process and the process with $qbar{q}$ pair and a photon in the final state. We provide a numerical evaluation of the photon spectrum from the annihilation process.
We calculate inclusive hadron productions in pA collisions in the small-x saturation formalism at one-loop order. The differential cross section is written into a factorization form in the coordinate space at the next-to-leading order, while the naive form of the convolution in the transverse momentum space does not hold. The rapidity divergence with small-x dipole gluon distribution of the nucleus is factorized into the energy evolution of the dipole gluon distribution function, which is known as the Balitsky-Kovchegov equation. Furthermore, the collinear divergences associated with the incoming parton distribution of the nucleon and the outgoing fragmentation function of the final state hadron are factorized into the splittings of the associated parton distribution and fragmentation functions, which allows us to reproduce the well-known DGLAP equation. The hard coefficient function, which is finite and free of divergence of any kind, is evaluated at one-loop order.
In the color string picture with fusion and percolation the dependence of the flow coefficients $v_n$ on the transverse momentum is studied for pp collisions the LHC energy respectively. Monte-Carlo simulations are used to locate simple strings and their fused clusters. The results favorably agree with the CMS data in the region $0.2 le p_tle 3.$ GeV/c appropriate for the string scenario.
The initial distribution of gluons at the very early times after a high energy heavy ion collision is described by the bulk scale $Q_s$ of gluon saturation in the nuclear wavefunction. The subsequent evolution of the system towards kinetic equilibrium is described by a non-linear Landau equation for the single particle distributions cite{Mueller1,Mueller2}. In this paper, we solve this equation numerically for the idealized initial conditions proposed by Mueller, and study the evolution of the system to equilibrium. We discuss the sensitivity of our results on the dynamical screening of collinear divergences. In a particular model of dynamical screening, the convergence to the hydrodynamic limit is seen to be rapid relative to hydrodynamic time scales. The equilibration time, the initial temperature, and the chemical potential are shown to have a strong functional dependence on the initial gluon saturation scale $Q_s$.
We present a new strategy using artificial intelligence (AI) to build the first AI-based Monte Carlo event generator (MCEG) capable of faithfully generating final state particle phase space in lepton-hadron scattering. We show a blueprint for integrating machine learning strategies with calibrated detector simulations to build a vertex-level, AI-based MCEG, free of theoretical assumptions about femtometer scale physics. As the first steps towards this goal, we present a case study for inclusive electron-proton scattering using synthetic data from the PYTHIA MCEG for testing and validation purposes. Our quantitative results validate our proof of concept and demonstrate the predictive power of the trained models. The work suggests new venues for data preservation to enable future QCD studies of hadrons structure, and the developed technology can boost the science output of physics programs at facilities such as Jefferson Lab and the future Electron-Ion Collider.
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