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Born-Oppenheimer renormalization group for high energy scattering: the setup and the wave function
No full textWe develop an approach to QCD evolution based on the sequential Born-Oppenheimer approximations that include higher and higher frequency modes as the evolution parameter is increased. This Born-Oppenheimer renormalization group is a general approach which is valid for the high energy evolution as well as the evolution in transverse resolution scale Q2. In the former case it yields the frequency ordered formulation of high energy evolution, which includes both the eikonal splittings which produce gluons with low longitudinal momentum, and the DGLAP-like splittings which produce partons with high transverse momentum. In this, first paper of the series we lay out the formulation of the approach, and derive the expression for the evolved wave function of a hadronic state. We also discuss the form of the S-matrix which is consistent with the frequency ordeing
Testing variational perturbation theory for effective actions using the Gaudin-Yang model
No full textConfirmation and extension of a mini-valence Wigner-like energy in Sm, Gd, and Dy
No full textAn earlier study revealed an unexpected enhancement in valence proton-neutron interaction strengths in certain heavy deformed even-even nuclei with equal numbers of valence protons and valence neutrons. The paper reports on an extension of those results to the entire span of deformed nuclei from Sm to W using mass data taken in the interim
Prominent Bump in the Two-Neutron Separation Energies of Neutron-Rich Lanthanum Isotopes Revealed by High-Precision Mass Spectrometry
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Angular differential and elemental fragmentation cross sections of a 400 MeV/nucleon beam on a graphite target with the FOOT experiment
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Injection optimization at particle accelerators via reinforcement learning: From simulation to real-world application
No full textOptimizing the injection process in particle accelerators is crucial for enhancing beam quality and operational efficiency. This paper presents a framework for utilizing reinforcement learning (RL) to optimize the injection process at accelerator facilities. By framing the optimization challenge as an RL problem, we developed an agent capable of dynamically aligning the beam’s transverse space with desired targets. Our methodology leverages the soft actor-critic algorithm, enhanced with domain randomization and dense neural networks, to train the agent in simulated environments with varying dynamics promoting it to learn a generalized robust policy. The agent was evaluated in live runs at the cooler synchrotron COSY and it has successfully optimized the beam cross section reaching human operator level but in notably less time. An empirical study further validated the importance of each architecture component in achieving a robust and generalized optimization strategy. The results demonstrate the potential of RL in automating and improving optimization tasks at particle acceleration facilities