An Optimal Observable Machine for reinterpretable measurements in high-energy physics

Torben Mohr, Alejandro Quiroga Triviño, Fabian Riemer +6

A machine-learning-based framework for constructing generator-level observables optimized for parameter extraction in particle physics analyses is introduced, referred to as the Op…