标题： 半监督旋转量去卷积及其在 MeerKAT 星系团观测中的应用 显示英文标题

标题： Semi-Supervised Rotation Measure Deconvolution and its application to MeerKAT observations of galaxy clusters

摘要： 法拉第旋转包含了视线方向上磁场结构的信息，是研究宇宙磁性的有力工具。传统的法拉第频谱去卷积方法，如 RMCLEAN，在解析复杂的法拉第色散函数和处理大数据集时面临挑战。我们开发了一种深度学习去卷积模型，以提高从射电天文数据中提取法拉第旋转量的准确性和效率，特别是针对来自 MeerKAT 星系团遗产调查（MGCLS）的数据。我们使用半监督学习，其中模型同时重构数据，并最小化输出与合成数据真实信号之间的差异。我们在模拟数据以及星系团 Abell 3376 的真实数据上进行了与 RMCLEAN 的性能比较。我们的半监督模型能够非常精确地恢复法拉第色散，特别是在复杂或高 RM 值信号的情况下，且在整个宽广的 RM 范围内保持灵敏度。该方法的计算效率显著优于传统方法。应用于 Abell 3376 的观测数据，我们发现了无线电遗迹和多个活动星系核（AGN）中的详细磁场结构。我们还应用该模型对 Abell 85、Abell 168、Abell 194、Abell 3186 和 Abell 3667 的 MeerKAT 数据进行了分析。 显示英文摘要

摘要： Faraday rotation contains information about the magnetic field structure along the line of sight and is an important instrument in the study of cosmic magnetism. Traditional Faraday spectrum deconvolution methods such as RMCLEAN face challenges in resolving complex Faraday dispersion functions and handling large datasets. We develop a deep learning deconvolution model to enhance the accuracy and efficiency of extracting Faraday rotation measures from radio astronomical data, specifically targeting data from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We use semi-supervised learning, where the model simultaneously recreates the data and minimizes the difference between the output and the true signal of synthetic data. Performance comparisons with RMCLEAN were conducted on simulated as well as real data for the galaxy cluster Abell 3376. Our semi-supervised model is able to recover the Faraday dispersion with great accuracy, particularly for complex or high-RM signals, maintaining sensitivity across a broad RM range. The computational efficiency of this method is significantly improved over traditional methods. Applied to observations of Abell 3376, we find detailed magnetic field structures in the radio relics, and several AGN. We also apply our model to MeerKAT data of Abell 85, Abell 168, Abell 194, Abell 3186 and Abell 3667.