Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications

ArXi:2603.13970v1 Announce Type: new In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena. Increasingly, deep learning models are applied to analyze both simulated and experimental data. In most experiments, a rigorous regime of testing for physically motivated systematic uncertainties is in place.