FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
hep-ph 3years
2026 3verdicts
UNVERDICTED 3roles
background 2representative citing papers
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
A primer that surveys the architecture, methodologies, computational challenges, and future trajectory of the Monte Carlo event generator ecosystem in collider physics.
citing papers explorer
-
Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
-
Open LHC Monte Carlo Event Generation
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
-
The Monte Carlo Ecosystem in High-Energy Physics: A Primer
A primer that surveys the architecture, methodologies, computational challenges, and future trajectory of the Monte Carlo event generator ecosystem in collider physics.