arXiv subjects

Yudong Ye

Publications and source records attributed to Yudong Ye.

4 recordsLinked to original sources

arXiv2026

The Internal Magnetic Field Structure of ICMEs in the Heliosphere

Interplanetary coronal mass ejections (ICMEs) are major drivers of heliospheric disturbances and space-weather effects. Here we present a multi-spacecraft study of 96 magnetic clouds (MCs) distributed over a broad range of heliocentric distances, and reconstruct their internal magnetic structure with a uniform-twist Gold--Hoyle (GH) flux-rope model. From the fits, we derive the axial field strength $B_0$, the twist density (turn density) $\tau$, the GH parameter $\omega$, and the integrated twist number $n$. We find that $B_0$ and the turn density $\tau$ both decrease with increasing heliocentric distance, consistent with expansion and axial stretching during propagation. A key result is that the upper envelope in the $\tau$--$R$ plane corresponds to a nearly constant boundary in the dimensionless GH parameter $\omega=2\pi R\tau$, close to $\omega\sim2$. Therefore, the inferred upper value of $\tau$ is not scale-independent, but follows $\tau_{\max}\simeq \omega_{\max}/(2\pi R)$ for a given flux-rope radius. In contrast, the estimated integrated turn number $n$ shows no similarly clear radial organization in the present sample. This study investigates the ICME structure and magnetic field characteristics across heliocentric distances from 0.07 to 5.4~AU, thereby providing observational constraints on the large-scale evolution of interplanetary magnetic flux ropes.

astro-ph.SR↗

arXiv2026

Magnetic Cloud Boundary Identification Using a Local-Normalized Magnetic Field Parameter

Due to the lack of quantitative and reproducible criteria for identifying magnetic cloud (MC) boundaries, we propose a parameter that characterizes short-timescale variability in magnetic field strength. The parameter, referred to as the Local-Normalized Magnetic field parameter (LNM), is defined as $\mathrm{LNM}(t)=\log_{10}\left(B(t)/\langle B \rangle_{\mathrm{5m\text{-}med}}(t)\right)$, where $B(t)$ is the total magnetic field strength and $\langle B \rangle_{\mathrm{5m\text{-}med}}(t)$ is its 5-minute running median ending at time $t$. This parameter measures the deviation of the magnetic field magnitude from its local background and reveals a clear contrast between the coherent magnetic structure inside MCs and the more variable ambient solar wind. Based on this parameter, we develop a semi-automated method for MC boundary identification, supported by Time Series Scalogram visualization. We further analyze 76 MC events using power spectral density (PSD) and slab fraction diagnostics. The results show that the dissipation-range spectral index inside MCs ($\sim f^{-2.21}$) is systematically smaller than that outside ($\sim f^{-2.59}$ and $f^{-2.89}$), and the slab fraction is reduced, indicating suppressed small-scale variability and enhanced anisotropy. These results support the applicability of the proposed parameter for MC boundary identification.

physics.space-ph↗

arXiv2018

A New Tool for CME Arrival Time Prediction Using Machine Learning Algorithms: CAT-PUMA

Coronal Mass Ejections (CMEs) are arguably the most violent eruptions in the Solar System. CMEs can cause severe disturbances in the interplanetary space and even affect human activities in many respects, causing damages to infrastructure and losses of revenue. Fast and accurate prediction of CME arrival time is then vital to minimize the disruption CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full-halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full-halo CMEs and using algorithms of the Support Vector Machine (SVM). We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions after applying CAT-PUMA to a test set, that is unknown to the engine, show a mean absolute prediction error $\sim$5.9 hours of the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hours. Comparison with other models reveals that CAT-PUMA has a more accurate prediction for 77% of the events investigated; and can be carried out very fast, i.e. within minutes after providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

astro-ph.SR↗

arXiv2018

Detailed analysis of dynamic evolution of three Active Regions before flare and CME occurrence at the photospheric level

We present a combined analysis of the applications of the weighted horizontal magnetic gradient (denoted as WG_M in Korsos et al., ApJ, 802, L21, 2015) method and the magnetic helicity tool (Berger & Field, JFM, 147, 133, 1984) employed for three active regions (ARs), namely NOAA AR11261, AR11283 and AR11429. All three active regions produced series flares and CMEs. We followed the evolution of the components of the WG_M and the magnetic helicity before the flare and CME occurrences. We found an unique and mutually shared behavior, called the U-shaped pattern, of the weighted distance component of WG_M and of the shearing component of the helicity flux before the flare and CME eruptions. This common pattern is associated with the decreasing-receding phase yet reported only known to be a necessary feature prior to solar flare eruption(s), but found now at the same time in the evolution of the shearing helicity parameter. This result leads to the conclusion that (i) the shearing motion of photospheric magnetic field may be a key driver for the solar eruption in addition to the flux emerging process, and that (ii) the found decreasing-approaching pattern in the evolution of shearing helicity may be another precursor indicator for improving the forecasting of solar eruptions.

astro-ph.SR↗