AI RESEARCH
Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
arXiv CS.LG
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ArXi:2605.07470v1 Announce Type: new Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedented performance in high-energy physics (HEP) analyses, it also makes them sensitive to small variations in their inputs. Consequently, the propagation and estimation of systematic uncertainties in NN-based models remain an open challenge.