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Found 10 papers, 2 papers with code
Reconstructing $S$-matrix Phases with Machine Learning
Unitarity relates them by an integral equation.
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality
Conversely, the correspondence allows one to engineer architectures realizing a given field theory by representing action deformations as deformations of neural network parameter densities.
Simplifying Polylogarithms with Machine Learning
Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a number of algebraic identities.
Challenges for Unsupervised Anomaly Detection in Particle Physics
We then show that optimal transport distances to representative events in the background dataset can be used directly for anomaly detection, with performance comparable to the autoencoders.
Binary JUNIPR: an interpretable probabilistic model for discrimination
We refer to this refined approach as Binary JUNIPR.
High Energy Physics - Phenomenology
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
As a third application, JUNIPR models can reweight events from one (e. g. simulated) data set to agree with distributions from another (e. g. experimental) data set.
Jet Charge and Machine Learning
Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider.
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.
