论文标题
Sroll3：一种神经网络方法，可减少普朗克高频仪器地图中大规模系统效应
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps
论文作者
论文摘要
在目前的工作中，我们提出了一种基于神经网络的数据反转方法，以减少结构化污染源，特别关注Planck高频仪器（Planck-HFI）数据的地图，并在生产的天空图中删除了大规模的系统效应。这些来源的结构化性质可以使污染源的去除，这是可以在不同时空尺度之间产生耦合的局部时空相互作用的特征。我们专注于探索神经网络，以利用这些耦合来学习最佳的低维度表示，并针对污染源的去除和制图目标进行了优化，以实现强大而有效的数据反演。我们开发了拟议方法的多种变体，并考虑包含物理知情的约束和转移学习技术。此外，我们专注于利用数据增强技术，以将专家知识整合到本来无监督的网络培训方法中。我们验证了Planck-HFI 545 GHz远侧叶仿真数据的建议方法，该方法考虑了涉及部分，缝隙填充和不一致数据集的理想和非理想案例，并证明了基于神经网络的维度降低的潜力，以准确模拟并消除大型系统效应。我们还提出了对真正的Planck-HFI 857 GHz数据的应用程序，该应用说明了所提出的方法对准确建模和捕获结构化污染源的相关性，据报道，就污染效果而言，据报道的增长率最高为一个数量级。重要的是，这项工作中开发的方法将集成到新版本的Sroll算法（Sroll3）中，我们在此处描述Sroll3 857 GHz检测器映射将发布给社区。
In the present work, we propose a neural network based data inversion approach to reduce structured contamination sources, with a particular focus on the mapmaking for Planck High Frequency Instrument (Planck-HFI) data and the removal of large-scale systematic effects within the produced sky maps. The removal of contamination sources is rendered possible by the structured nature of these sources, which is characterized by local spatiotemporal interactions producing couplings between different spatiotemporal scales. We focus on exploring neural networks as a means of exploiting these couplings to learn optimal low-dimensional representations, optimized with respect to the contamination source removal and mapmaking objectives, to achieve robust and effective data inversion. We develop multiple variants of the proposed approach, and consider the inclusion of physics informed constraints and transfer learning techniques. Additionally, we focus on exploiting data augmentation techniques to integrate expert knowledge into an otherwise unsupervised network training approach. We validate the proposed method on Planck-HFI 545 GHz Far Side Lobe simulation data, considering ideal and non-ideal cases involving partial, gap-filled and inconsistent datasets, and demonstrate the potential of the neural network based dimensionality reduction to accurately model and remove large-scale systematic effects. We also present an application to real Planck-HFI 857 GHz data, which illustrates the relevance of the proposed method to accurately model and capture structured contamination sources, with reported gains of up to one order of magnitude in terms of contamination removal performance. Importantly, the methods developed in this work are to be integrated in a new version of the SRoll algorithm (SRoll3), and we describe here SRoll3 857 GHz detector maps that will be released to the community.
