RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
Butteret al., SciPost Phys.14, 079 (2023), arXiv:2203.07460 [hep-ph]
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
hep-ph 5roles
background 1polarities
background 1representative citing papers
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.
AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupling-layer flows.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
citing papers explorer
-
Reweighting Adversarial Networks for Unbinned Unfolding
RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
-
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.
-
AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements
AI-driven symbolic evolution discovers interpretable event-level observables that retain substantially more local Fisher information than angular baselines for CP-sensitive HZ interference in two collider channels.
-
Monte Carlo Event Generation with Continuous Normalizing Flows
Continuous normalizing flows improve unweighting efficiency in Monte Carlo event generation for high-jet-multiplicity collider processes by factors up to 184, with wall-time gains of about ten when combined with coupling-layer flows.
-
Amplitude Uncertainties Everywhere All at Once
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.