Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparison

While the interaction of ultra-intense ultra-short laser pulseswith near- and overcritical plasmas cannot be directly observed, experimentally accessible quantities (observables) often only indirectly giveinformation about the underlying plasma dynamics. Furthermore, theinformation provided by observables is incomplete, making the inverseproblem highly ambiguous. Therefore, in order to infer plasma dynamicsas well as experimental parameter, the full distribution over parameters given an observation needs to considered, requiring that models areflexible and account for the information lost in the forward process. Invertible Neural Networks (INNs) have been designed to efficiently modelboth the forward and inverse process, providing the full conditional posterior given a specific measurement. In this work, we benchmark INNsand standard statistical methods on synthetic electron spectra. First, weprovide experimental results with respect to the acceptance rate, whereour results show increases in acceptance rates up to a factor of 10. Additionally, we show that this increased acceptance rate also results in anincreased speed-up for INNs to the same extent. Lastly, we propose acomposite algorithm that utilizes INNs and promises low runtimes whilepreserving high accuracy.