一种用于帕克太阳探测器（Parker Solar Probe）观测岁星（Jupiter）射电辐射的奇异值分解（Singular Value Decomposition）框架

本文提出了一种利用奇异值分解和双阶段噪声过滤的通用经验流程，旨在从帕克太阳探测器观测到的太阳风背景中提取微弱的岁星射电辐射信号。该模型通过将动态谱转换为周期性相位折叠参考系，显著提升了信噪比，并为识别长期全球形态趋势提供了一种自动化检测方法。

arXiv:2608.25075v1 Announce Type: new Abstract: Jovian decametric and hectometric radio emissions provide critical insights into Jupiter's magnetospheric dynamics and its electrodynamic coupling with Io. Across 21 perihelion encounters since its first light in 2018, the Parker Solar Probe mission has repeatedly recorded high-resolution radio and plasma wave data near closest approach, as a result serving as a long-term distant observer of the Jovian system. However, extracting these relatively faint planetary signals from the continuous solar wind background presents a significant analytical challenge, as the data is routinely saturated by quasi-thermal plasma noise, spacecraft instrumental interference, and solar bursts. To overcome these observational barriers, we present a generalized empirical pipeline that utilizes Singular Value Decomposition coupled with a deterministic, dual-stage noise filtering process. By converting dynamic spectra into a periodic, phase-folded reference frame, this model isolates structured Jovian emissions from the stochastic heliospheric background, demonstrating empirical Signal-to-Noise Ratio improvements of $1.54 \pm 0.11$ dB, reaching up to $3.32$ dB. Additionally, a secondary ``Eigenfaces'' matrix factorization is applied to identify long-term global morphological trends and serve as a detection method for future Jovian emissions. This automated mathematical framework extracts transient planetary signals without relying on localized spatial constraints, establishing the viability of