用补充数据锐化银河系全天空地图-机器学习方法
Sharpening up Galactic all-sky maps with complementary data - A machine learning approach
摘要：
在非常不同的频率上，如在射电和$γ$射线区域，银河系的全天空地图显示出相似的形态结构。这种互信息反映了星际介质的各种物理成分的印记。我们希望使用多频全天观测来测试分辨率的提高和某些频率范围内未观测区域的地图恢复。为此，我们的目标是从其他地图的集合中重建或预测全天空地图，这些地图在其原始形式中，与其他可用的全天空调查相比缺乏高分辨率，或者在其空间覆盖范围中不完整。此外，我们还想研究ISM组件在电磁波谱上表现出的共性和差异。我们建立了一个$n$维表示的联合像素亮度分布的$n$地图使用高斯混合模型，看看它是如何预测的：如何以及可以复制一个地图的基础上，其他地图的子集？模拟数据的测试表明，从其他频率区域重建特定频率的地图效果非常好，可靠地预测小尺度细节，远低于最初学习的地图的空间分辨率。应用于观测到的银河系多频数据集，这种技术能够提高分辨率，例如，低分辨率费米LAT地图，以及从人为污染的数据，如ROSAT 0.855 keV地图恢复天空。与原始图像相比，预测的图像通常显示较少的成像伪影。预测图和原始图的比较突出了令人惊讶的结构，成像伪影（幸运的是在预测中没有再现），以及在其他频带不存在的相应频率范围的真实特征。我们讨论了这种机器学习方法的局限性以及如何克服它们的想法。
英文摘要：
Galactic all-sky maps at very disparate frequencies, like in the radio and $γ$-ray regime, show similar morphological structures. This mutual information reflects the imprint of the various physical components of the interstellar medium. We want to use multifrequency all-sky observations to test resolution improvement and restoration of unobserved areas for maps in certain frequency ranges. For this we aim to reconstruct or predict from sets of other maps all-sky maps that, in their original form, lack a high resolution compared to other available all-sky surveys or are incomplete in their spatial coverage. Additionally, we want to investigate the commonalities and differences that the ISM components exhibit over the electromagnetic spectrum. We build an $n$-dimensional representation of the joint pixel-brightness distribution of $n$ maps using a Gaussian mixture model and see how predictive it is: How well can one map be reproduced based on subsets of other maps? Tests with mock data show that reconstructing the map of a certain frequency from other frequency regimes works astonishingly well, predicting reliably small-scale details well below the spatial resolution of the initially learned map. Applied to the observed multifrequency data sets of the Milky Way this technique is able to improve the resolution of, e.g., the low-resolution Fermi LAT maps as well as to recover the sky from artifact-contaminated data like the ROSAT 0.855 keV map. The predicted maps generally show less imaging artifacts compared to the original ones. A comparison of predicted and original maps highlights surprising structures, imaging artifacts (fortunately not reproduced in the prediction), and features genuine to the respective frequency range that are not present at other frequency bands. We discuss limitations of this machine learning approach and ideas how to overcome them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
