We performed batched Bayesian Optimization with both the Ax and BOTorch library in a single and multi-target configuration. For the single task setting we set the objective to be the sum or difference of normalized lifetime and normalized initial size (or average intensity). For the multi-label settings, we set the objective to maximize the space of all labels. For BOTorch we chose a MixedSingleTaksGP with SobolQMCNormalSampler and qExpectedImprovement for optimization in the single target setting. A list of MixedSingleTaksGP with qNoisyExpectedHypervolumeImprovement in the multi-target setting. For Ax we used the Service API and skipped the initial suggestion. All predictions of the different approaches were combined for the next experimental measurement round.