Jet energy calibration hands each stage to a neural network: residual pile-up dependence falls to about a tenth of the parametric baseline
Hadronic jet energy calibration at the Large Hadron Collider has relied on fitting parametric functional forms to selected observables, correcting pile-up, detector response and data–simulation differences in sequence. A prototype pipeline instead trains a neural network for each stage directly against the quantity that stage removes, imposes no functional form, and extends by adding inputs. On simulated dijet and large-radius jet samples it matches or exceeds the parametric baseline at nearly every stage, with residual pile-up dependence averaging about a tenth of the baseline's.