Sensitivity Calculation for Monte Carlo Particle Simulations of Neutrals in the Plasma Edge

Algorithmic differentiation (AD) is a promising tool to accurately and efficiently compute sensitivities of several code outputs w.r.t. the code input parameters for the complex Monte Carlo code EIRENE. We illustrate the procedure by assessing the sensitivity of the total atom content w.r.t. the atomic and molecular cross-sections and rate coefficients. AD does not suffer from decorrelation between primal and perturbed particle trajectories, which easily happens for finite difference (FD) sensitivity calculations. Consequently, the resulting statistical errors on AD sensitivities are up to five orders of magnitude smaller than the statistical errors on FD sensitivities for a JET low-recycling background plasma. Although this statistical error reduction is promising for the application of the AD sensitivities in a future optimization or uncertainty quantification framework, we show that the AD sensitivities can blow up for a minority of long-lived particles, especially occurring in detachment. Finally, we conclude that the Monte Carlo simulation and estimator type possibly need to be adapted to improve the accuracy of the sensitivities.