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Found 4 papers, 1 papers with code
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Revealing the structure of light pseudoscalar mesons at the Electron-Ion Collider
How the bulk of the Universe's visible mass emerges and how it is manifest in the existence and properties of hadrons are profound questions that probe into the heart of strongly interacting matter.
Nuclear Experiment High Energy Physics - Phenomenology Nuclear Theory
First analysis of world polarized DIS data with small-$x$ helicity evolution
We present a Monte Carlo based analysis of the combined world data on polarized lepton-nucleon deep-inelastic scattering at small Bjorken $x$ within the polarized quark dipole formalism.
High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment Nuclear Theory
Asymmetric jet clustering in deep-inelastic scattering
We propose a new jet algorithm for deep-inelastic scattering (DIS) that accounts for the forward-backward asymmetry in the Breit frame.
High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory
