Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
Kogleret al., Jet Substructure at the Large Hadron Collider: Experimental Review, Rev
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Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.
Observing vector boson fusion production of a heavy scalar followed by decay to Z h1 at muon colliders can establish CP violation in the scalar sector because it requires both h2VV and h2h1Z couplings to be nonzero.
citing papers explorer
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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.
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Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.
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Model-independent probes of CP violation in the heavy scalar sector at muon colliders
Observing vector boson fusion production of a heavy scalar followed by decay to Z h1 at muon colliders can establish CP violation in the scalar sector because it requires both h2VV and h2h1Z couplings to be nonzero.
- Looking inside jets: an introduction to jet substructure and boosted-object phenomenology