Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
Gavrikov et al.,Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline, arXiv preprint arXiv:2405.00000 (2024)
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Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.