Optimising HEP parameter fits via Monte Carlo weight derivative regression

Understand

HEP event selection is traditionally considered a binary classification problem, involving the dichotomous categories of signal and background.

• In distribution fits for particle masses or couplings, however, signal events are not all equivalent, as the signal differential cross section has different sensitivities to the measured parameter in different regions of phase space.
• In this paper, I describe a mathematical framework for the evaluation and optimization of HEP parameter fits, where this sensitivity is defined on an event-by-event basis, and for MC events it is modeled in terms of their MC weight derivatives with respect to the measured parameter.
• Minimising the statistical error on a measurement implies the need to resolve (i.e.