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Dynamical Origin of (469219) Kamo`oalewa of Tianwen-2 Mission from the Main-Belt: $ν_6$ Secular Resonance, Flora Family or 3:1 Resonance with Jupiter
Authors:
Yandong Wang,
Shoucun Hu,
Jianghui Ji,
Jiajun Ying
Abstract:
China's Tianwen-2 mission, launched on 29 May 2025, targets the near-Earth object (469219) Kamo`oalewa, an Earth quasi-satellite trapped in a 1:1 mean-motion resonance with our planet. Determining the origin of Kamo`oalewa is central to understanding the formation pathways and dynamical evolution of Earth's quasi-satellite population. Here we show a strong possibility of main-belt origin for Kamo`…
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China's Tianwen-2 mission, launched on 29 May 2025, targets the near-Earth object (469219) Kamo`oalewa, an Earth quasi-satellite trapped in a 1:1 mean-motion resonance with our planet. Determining the origin of Kamo`oalewa is central to understanding the formation pathways and dynamical evolution of Earth's quasi-satellite population. Here we show a strong possibility of main-belt origin for Kamo`oalewa using long-term dynamical simulations. We examine three candidate source regions: the $ν_6$ secular resonance ($ν_6$), the 3:1 mean-motion resonance with Jupiter (3:1J MMR), and the Flora family. A total of 42,825 test particles were integrated over 100 Myr. We find that asteroids from all three regions can be transported onto Kamo`oalewa-like orbits, albeit with markedly different efficiencies. Particles originating near the $ν_6$ show the highest transfer probability (3.31%), followed by the Flora family (2.54%) and the 3:1J MMR (0.39%). We further identify representative dynamical pathways linking these source regions to Earth quasi-satellite orbits. The Tianwen-2 spacecraft is expected to rendezvous with Kamo`oalewa in 2026, performing close-proximity operations and returning samples. The mission will provide decisive observational constraints on the asteroid's composition and physical properties, offering a critical test of its proposed origin.
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Submitted 19 January, 2026;
originally announced January 2026.
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A Neural Symbolic Model for Space Physics
Authors:
Jie Ying,
Haowei Lin,
Chao Yue,
Yajie Chen,
Chao Xiao,
Quanqi Shi,
Yitao Liang,
Shing-Tung Yau,
Yuan Zhou,
Jianzhu Ma
Abstract:
In this study, we unveil a new AI model, termed PhyE2E, to discover physical formulas through symbolic regression. PhyE2E simplifies symbolic regression by decomposing it into sub-problems using the second-order derivatives of an oracle neural network, and employs a transformer model to translate data into symbolic formulas in an end-to-end manner. The resulting formulas are refined through Monte-…
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In this study, we unveil a new AI model, termed PhyE2E, to discover physical formulas through symbolic regression. PhyE2E simplifies symbolic regression by decomposing it into sub-problems using the second-order derivatives of an oracle neural network, and employs a transformer model to translate data into symbolic formulas in an end-to-end manner. The resulting formulas are refined through Monte-Carlo Tree Search and Genetic Programming. We leverage a large language model to synthesize extensive symbolic expressions resembling real physics, and train the model to recover these formulas directly from data. A comprehensive evaluation reveals that PhyE2E outperforms existing state-of-the-art approaches, delivering superior symbolic accuracy, precision in data fitting, and consistency in physical units. We deployed PhyE2E to five applications in space physics, including the prediction of sunspot numbers, solar rotational angular velocity, emission line contribution functions, near-Earth plasma pressure, and lunar-tide plasma signals. The physical formulas generated by AI demonstrate a high degree of accuracy in fitting the experimental data from satellites and astronomical telescopes. We have successfully upgraded the formula proposed by NASA in 1993 regarding solar activity, and for the first time, provided the explanations for the long cycle of solar activity in an explicit form. We also found that the decay of near-Earth plasma pressure is proportional to r^2 to Earth, where subsequent mathematical derivations are consistent with satellite data from another independent study. Moreover, we found physical formulas that can describe the relationships between emission lines in the extreme ultraviolet spectrum of the Sun, temperatures, electron densities, and magnetic fields. The formula obtained is consistent with the properties that physicists had previously hypothesized it should possess.
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Submitted 29 October, 2025; v1 submitted 10 March, 2025;
originally announced March 2025.
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Terahertz-driven Two-Dimensional Mapping for Electron Temporal Profile Measurement
Authors:
Xie He,
Jiaqi Zheng,
Dace Su,
Jianwei Ying,
Lufei Liu,
Hongwen Xuan,
Jingui Ma,
Peng Yuan,
Nicholas H. Matlis,
Franz X. Kartner,
Dongfang Zhang,
Liejia Qian
Abstract:
The precision measurement of real-time electron temporal profiles is crucial for advancing electron and X-ray devices used in ultrafast imaging and spectroscopy. While high temporal resolution and large temporal window can be achieved separately using different technologies, real-time measurement enabling simultaneous high resolution and large window remains challenging. Here, we present the first…
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The precision measurement of real-time electron temporal profiles is crucial for advancing electron and X-ray devices used in ultrafast imaging and spectroscopy. While high temporal resolution and large temporal window can be achieved separately using different technologies, real-time measurement enabling simultaneous high resolution and large window remains challenging. Here, we present the first THz-driven sampling electron oscilloscope capable of measuring electron pulses with high temporal resolution and a scalable, large temporal window simultaneously. The transient THz electric field induces temporal electron streaking in the vertical axis, while extended interaction along the horizontal axis leads to a propagation-induced time delay, enabling electron beam sampling with sub-cycle THz wave. This allows real-time femtosecond electron measurement with a tens-of-picosecond window, surpassing previous THz-based techniques by an order of magnitude. The measurement capability is further enhanced through projection imaging, deflection cavity tilting, and shorted antenna utilization, resulting in signal spatial magnification, extended temporal window, and increased field strength. The technique holds promise for a wide range of applications and opens new opportunities in ultrafast science and accelerator technologies.
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Submitted 5 December, 2024;
originally announced December 2024.
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Accelerated Magnetic Resonance Spectroscopy with Vandermonde Factorization
Authors:
Xiaobo Qu,
Jiaxi Ying,
Jian-Feng Cai,
Zhong Chen
Abstract:
Multi-dimensional magnetic resonance spectroscopy is an important tool for studying molecular structures, interactions and dynamics in bio-engineering. The data acquisition time, however, is relatively long and non-uniform sampling can be applied to reduce this time. To obtain the full spectrum,a reconstruction method with Vandermonde factorization is proposed.This method explores the general sign…
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Multi-dimensional magnetic resonance spectroscopy is an important tool for studying molecular structures, interactions and dynamics in bio-engineering. The data acquisition time, however, is relatively long and non-uniform sampling can be applied to reduce this time. To obtain the full spectrum,a reconstruction method with Vandermonde factorization is proposed.This method explores the general signal property in magnetic resonance spectroscopy: Its time domain signal is approximated by a sum of a few exponentials. Results on synthetic and realistic data show that the new approach can achieve faithful spectrum reconstruction and outperforms state-of-the-art low rank Hankel matrix method.
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Submitted 24 January, 2017;
originally announced January 2017.
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A Hybrid Solver of Size Modified Poisson-Boltzmann Equation by Domain Decomposition, Finite Element, and Finite Difference
Authors:
Jinyong Ying,
Dexuan Xie
Abstract:
The size-modified Poisson-Boltzmann equation (SMPBE) is one important variant of the popular dielectric model, the Poisson-Boltzmann equation (PBE), to reflect ionic size effects in the prediction of electrostatics for a biomolecule in an ionic solvent. In this paper, a new SMPBE hybrid solver is developed using a solution decomposition, the Schwartz's overlapped domain decomposition, finite eleme…
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The size-modified Poisson-Boltzmann equation (SMPBE) is one important variant of the popular dielectric model, the Poisson-Boltzmann equation (PBE), to reflect ionic size effects in the prediction of electrostatics for a biomolecule in an ionic solvent. In this paper, a new SMPBE hybrid solver is developed using a solution decomposition, the Schwartz's overlapped domain decomposition, finite element, and finite difference. It is then programmed as a software package in C, Fortran, and Python based on the state-of-the-art finite element library DOLFIN from the FEniCS project. This software package is well validated on a Born ball model with analytical solution and a dipole model with a known physical properties. Numerical results on six proteins with different net charges demonstrate its high performance. Finally, this new SMPBE hybrid solver is shown to be numerically stable and convergent in the calculation of electrostatic solvation free energy for 216 biomolecules and binding free energy for a DNA-drug complex.
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Submitted 19 October, 2016;
originally announced October 2016.
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Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Authors:
Jiaxi Ying,
Hengfa Lu,
Qingtao Wei,
Jian-Feng Cai,
Di Guo,
Jihui Wu,
Zhong Chen,
Xiaobo Qu
Abstract:
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. But existing approaches can not efficiently recover $N$-dimensional exponential si…
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Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. But existing approaches can not efficiently recover $N$-dimensional exponential signals with $N\geq 3$. In this paper, we study the problem of recovering N-dimensional (particularly $N\geq 3$) exponential signals from partial observations, and formulate this problem as a low-rank tensor completion problem with exponential factor vectors. The full signal is reconstructed by simultaneously exploiting the CANDECOMP/PARAFAC structure and the exponential structure of the associated factor vectors. The latter is promoted by minimizing an objective function involving the nuclear norm of Hankel matrices. Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.
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Submitted 31 March, 2017; v1 submitted 6 April, 2016;
originally announced April 2016.