The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN~(IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the $pp \to Z \gamma \gamma$ process.

翻译：探测器效应的演进对于将数据与理论预测进行比较至关重要。 虽然传统方法仅限于代表低维数的数据, 但机器学习在保留完整维度的同时, 也带来了新的开发技术。 生成网络, 如垂直神经网络~( INN) 能够产生概率性演进, 将个别事件映射为相应的演进概率分布。 然而, 这些方法的准确性受到模拟培训样本模型如何模拟实际数据所显示的准确性的限制。 我们引入了可调整模拟培训样本和数据偏差的迭接性 IMN~( IcINN) 。 IcINN 的演进首先在玩具数据上验证, 然后应用到 $pp\to Z\ gamma \ gamma$ 过程的假数据 。