
The core message of this paper is to use machine learning to design observables that are maximally sensitive to target parameters or models while also being minimally sensitive to detector distortions, in order to improve the precision and robustness of parameter estimation and model discrimination in particle and nuclear physics analyses.


coremsg

designing-observables-for-measurements-with-deep-learning-to-improve-sensitivity-and-reduce-detector-distortions


Designing Observables for Measurements with Deep Learning to Improve Sensitivity and Reduce Detector Distortions
