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Found 6 papers, 3 papers with code
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data.
OmniFold: A Method to Simultaneously Unfold All Observables
Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments.
Energy Flow Networks: Deep Sets for Particle Jets
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events.
Learning to Classify from Impure Samples with High-Dimensional Data
In particle physics, this challenge is surmounted by the use of simulations.
Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup).
Deep learning in color: towards automated quark/gluon jet discrimination
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics.
