# Dynamic Test Particle Simulations of X-Ray Emissions at Dayside Magnetopause Under Time-Varying Solar Wind Conditions

Qiuyu Xu, Dimitra Koutroumpa, Ronan Modolo, Binbin Tang

► To cite this version:

Qiuyu Xu, Dimitra Koutroumpa, Ronan Modolo, Binbin Tang. Dynamic Test Particle Simulations of X-Ray Emissions at Dayside Magnetopause Under Time-Varying Solar Wind Conditions. Journal of Geophysical Research Space Physics, 2025, 130 (12), pp.e2025JA034416. 10.1029/2025ja034416. hal-05425272

HAL Id: hal-05425272

https://hal.science/hal-05425272v1

Submitted on 19 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# JGR Space Physics

## RESEARCH ARTICLE
10.1029/2025JA034416

**Key Points:**

* New 3D global dynamic test-particle model is developed to simulate X-ray emissions under time-varying solar wind conditions

* The D-LaTeP model has been tested under a real-time Interplanetary Magnetic Field (IMF) southward turning event and a dynamic pressure enhancement event

* Time-averaged intensity maps imply that imaging missions could in principle capture magnetospheric dynamics

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

Q. Xu,
qiuyu.xu@latmos.ipsl.fr

**Citation:**

Xu, Q., Koutroumpa, D., Modolo, R., & Tang, B. (2025). Dynamic Test Particle simulations of X-ray emissions at dayside magnetopause under time-varying Solar Wind conditions. *Journal of Geophysical Research: Space Physics*, 130, e2025JA034416. https://doi.org/10.1029/2025JA034416

Received 22 JUL 2025
Accepted 21 NOV 2025

<figure><img src="image_2.png" /></figure>

# Dynamic Test Particle Simulations of X-Ray Emissions at Dayside Magnetopause Under Time-Varying Solar Wind Conditions

Qiuyu Xu¹ ID, Dimitra Koutroumpa¹ ID, Ronan Modolo¹ ID, and Binbin Tang² ID

¹LATMOS/IPSL, CNRS, UVSQ Université Paris-Saclay, Sorbonne Université, CNES, Guyancourt, France, ²National Space Science Center, Chinese Academy of Sciences, Beijing, China

**Abstract** The Latmos Test Particle (LaTeP) model is a steady-state test particle code to investigate the solar wind charge exchange soft X-ray emissions of the steady-state Earth magnetosphere. In this study, we present the development of a new dynamic version (D-LaTeP) extending the LaTeP model to adapt to time-varying solar wind conditions. To test and validate the D-LaTeP model, we simulate X-ray emissions using E & B input from the Magnetohydrodynamics model with 1-min resolution for a real-time Interplanetary Magnetic Field (IMF) southward turning event and a dynamic pressure enhancement event. The model can simulate the temporal evolution of X-ray emissions near magnetopause under both variations in magnetic field and dynamic pressure, and mimics the relative motion of the boundary, indicating its robustness under time-varying solar wind conditions. In order to provide observation-like conditions for future comparisons with X-ray imaging missions (e.g., Solar wind Magnetosphere Ionosphere Link Explorer, Lunar Environment Heliospheric X-ray Imager), we calculate the 5-min and 10-min average of the intensity maps, which are obtained from virtual ideal imagers. The 5-min and 10-min averaged intensity maps reveal clear motions of the magnetopause and cusps, suggesting in principle the capability of the imaging missions to detect the evolution of the magnetosphere under ideal observing conditions.

**Plain Language Summary** The LATMOS Test Particle (LaTeP) model can simulate the X-ray emission under steady-state solar wind conditions. In this study, we develop a new dynamic version D-LaTeP model to apply to time-varying solar wind conditions. To test and validate the model, we perform D-LaTeP simulations under real-time IMF southward turning event and a dynamic pressure enhancement event. The results indicate that the model can display the evolution of X-ray emissions near the magnetosphere for both cases, indicating its robustness under time-varying solar wind conditions. To compare observations from X-ray imaging missions like Solar wind Magnetosphere Ionosphere Link Explorer and Lunar Environment Heliospheric X-ray Imager with our results, we averaged the simulated X-ray images over 5 and 10 min. These averaged images clearly show the movement of the magnetopause and cusps, indicating that X-ray missions with similar exposure times could in principle capture these temporal variations effectively under ideal observing conditions.

## 1. Introduction

Solar Wind Charge Exchange (SWCX) is a fundamental astrophysical process characterized by the interaction between high-valence solar wind ions (e.g., C⁵⁺, C⁶⁺, N⁶⁺, N⁷⁺, O⁷⁺ and O⁸⁺) and neutral particles (e.g., H), resulting in the emission of soft X-ray or UV photons (Cravens, 1997). This phenomenon has been extensively observed not only at Earth (Ringuette et al., 2023; Y. Zhang et al., 2023), Mars (Dennerl et al., 2006), Jupiter (Dunn et al., 2022), comets (Cravens, 1997; Wedlund et al., 2020), and the Moon (Robertson et al., 2009) but also when interstellar neutrals flow through the heliosphere (Ringuette et al., 2021). The SWCX emission mechanism is illustrated by the following equations:

$$X^{q+} + M \rightarrow X^{(q-1)+*} + M^{+} \quad (1)$$

$$X^{(q-1)+*} \rightarrow X^{(q-1)+} + h\nu, \quad (2)$$

© 2025. The Author(s).
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
where the heavy high-valence solar wind ion is noted as X$^{q+}$ and the neutral particle is noted as M. Generally, an electron is captured by the high-valence solar wind ion from the neutral atom or molecule. This transfer leaves the heavy ion in an excited state, leading to the emission of one or multiple photons in soft X-ray band as it transitions to a lower energy state.

The emission of the X-ray photons is proportional to the solar wind ion flux and to the density of the neutral particles. Therefore, in regions characterized by a high density of solar wind ions and neutral particles, such as the Earth's dayside magnetosheath and polar cusps, the soft X-ray emission is enhanced. This enhancement of X-ray emissions at these key regions allows global images of Earth's magnetosphere using space-born soft X-ray imaging instruments. The forthcoming Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission, a collaborative mission between the European Space Agency and the Chinese Academy of Sciences scheduled for launch in early 2026, will exploit SWCX imaging of plasma density structures to investigate the solar wind-magnetosphere coupling system (Branduardi-Raymont et al., 2018; Wang & Branduardi-Raymont, 2018). SMILE will carry a soft X-ray Imager (SXI) with a large field-of-view (15.5° × 26.5°), which allows it to globally image the Earth's magnetosphere (Sembay et al., 2023).

Since no large-scale soft X-ray images of the magnetosphere have been observed to date, conducting simulations of X-ray emissions is important for the preparation of the mission, and to anticipate the analysis of estimated images from the upcoming SMILE mission with a modeling activity. Magnetohydrodynamic (MHD) models have been extensively used to simulate the X-ray emission in the near Earth environment. Robertson et al. (2006) simulated X-ray emissions from magnetosheath and cusps. By comparing ROSAT All-Sky Survey with MHD simulations, Kuntz et al. (2015) derived the SWCX production factor for hydrogen. Whittaker et al. (2016) performed MHD simulations to compare with XMM-Newton X-ray observations. The dynamics of X-ray emissions near the magnetosheath under different solar wind conditions are investigated using the MHD model (Gong et al., 2025; Sun et al., 2019). Momose et al. (2025) use MHD model to simulate the X-ray emission at the dayside magnetopause, further estimating the reconnection rate from the X-ray emissions. Kim et al. (2023) developed a method to estimate the subsolar magnetopause position from MHD simulated X-ray images. Besides MHD models, other models such as hybrid model (Liang et al., 2023; Yang et al., 2023; Z. Zhang et al., 2024) and test particle model (Koutroumpa et al., 2012; Tkachenko et al., 2021; Xu et al., 2024) are also frequently used to investigate the X-ray emissions around Earth or other planetary bodies. Sibeck et al. (2018) reviewed the theory, simulations, and observations of the SWCX emissions.

Previously, we have simulated X-ray emissions using the LATMOS Test Particle (LaTeP) model (Xu et al., 2024), which is based on a test particle Monte Carlo approach under external MHD electro-magnetic field input. The approach of coupling MHD-Test-particle is valid since the high-valence solar wind ions represent a very small fraction of the solar wind population and therefore can be assumed not to contribute to the global current system and affect the solar wind-magnetosphere interaction. The results indicate that compared to MHD models, there are several advantages that LaTeP model introduces. First, the LaTeP Model has separate ion species calculations, thus can investigate the spectral characteristics for each ion species. Second, the LaTeP Model introduces kinetic effect, due to the gyromotion of the test-particles around the magnetic field lines. Third, the LaTeP Model can disentangle the solar wind and magnetospheric plasma. However, the LaTeP model requires external electro-magnetic field input, which limits its application to a steady or quasi-steady state magnetosphere. In reality, the solar wind conditions are highly dynamic, resulting in a time varying magnetosphere. Therefore, in this study, we aim to improve the LaTeP model to conduct simulations based on time-dependent solar wind conditions.

We have developed a novel approach based on a Dynamic LATMOS Test Particle (D-LaTeP) model to apply to dynamic electro-magnetic field input. In Section 2.1, the general workflow of the newly developed D-LaTeP model is introduced. In Sections 2.2 and 2.3, we perform benchmark testing on the number of particles launched at every time step, and the total running time needed for the simulation to reach steady-state-like conditions. In Section 3, to test and validate the D-LaTeP model under varying solar wind conditions, we simulate the X-ray emission using E&B input from the Piecewise Parabolic Method with a Lagrangian Remap (PPMLR) MHD model (Hu et al., 2005) under an observed real-time Interplanetary Magnetic Field (IMF) southward turning event and a dynamic pressure enhancement event. The conclusion and discussion is indicated in Section 5 and Section 4, respectively.
## 2. Method

As indicated in Equations 1 and 2, photons in soft X-ray band are emitted during the SWCX process. The volume emissivity of the generated X-ray can be derived from the following equation:

$$Q = n_M \cdot n_{X^{q+}} \cdot v_{X^{q+}} \cdot \sigma_{X^{q+},M} \cdot Y_{X^{(q-1)+}}, \quad (3)$$

where Q indicates volume emissivity (eV cm⁻³ s⁻¹). The n_M and n_{X^{q+}} indicate the number density (cm⁻³) of neutral particles and solar wind ions, respectively. The v_{X^{q+}} indicates the plasma velocity (cm s⁻¹), including the bulk and thermal components. The σ_{X^{q+},M} indicates the cross-section (cm²) of the collision between neutral particle and solar wind ion. Y_{X^{(q-1)+}} indicates the emission line energy (eV) weighted by the emission probability of each transition. Here we use the KRONOS (Cumbee et al., 2021) database to determine the value of the cross-section σ_{X^{q+},M}. Y_{X^{(q-1)+}} is derived from the results of previous studies on the SWCX mechanism (Kharchenko, 2005; Kharchenko & Dalgarno, 2000; Koutroumpa et al., 2006). In the case of the Earth's exosphere, we only consider atomic hydrogen as the neutral targets.

Connor and Carter (2019) and Jung et al. (2022) point out that the exospheric neutral density near dayside magnetopause is time-dependent and may evolve during the event. However, for simplicity and to better characterize the impact of the solar wind varying conditions, here we use a simple equation (Cravens et al., 2001; Hodges Jr, 1994) instead of a sophisticated model to estimate the exospheric density:

$$n_M = n_0 \left( \frac{10 R_E}{R} \right)^3 (\text{cm}^{-3}), \quad (4)$$

where R indicates the distance from the Earth's center in the unit of R_E, n_0 indicates the neutral density at 10 R_E subsolar point, which is a characteristic neutral density and equals 25 cm⁻³ in this study.

### 2.1. D-LaTeP Model

The D-LaTeP model is an improved version of the previous LaTeP model (Xu et al., 2024). In the LaTeP model, we launch test particles one by one, track their motion through the stable electromagnetic field, and accumulate X-ray emissions they produced one by one along the trajectories to represent the emission contribution. In the D-LaTeP model, the particles are launched and pushed simultaneously at each time step to adapt to time-dependent electro-magnetic field. We derive the X-ray emission based on the locations of all the test particles, thus the X-ray emissions in D-LaTeP model correspond to a snapshot of the environment.

The D-LaTeP does not self compute the electro-magnetic field. The D-LaTeP model only simulates the motion and behavior of the test particles under externally input electro-magnetic field (e.g., from Hybrid, MHD, and other models). The simulation domain of the D-LaTeP model mainly concentrates on the dayside magnetosphere, and set to be 0 ≤ X ≤ 19 R_E, -10 ≤ Y ≤ 10 R_E, and -10 ≤ Z ≤ 10 R_E in the GSM coordinate system. To well illustrate kinetic aspects of the test-particle dynamics and present small-scale structures, we set the spatial resolution to be 0.05 R_E. The time step of the simulation is set to be 0.05 s. The general workflow of the D-LaTeP model in one time step is:

First, the electro-magnetic field in the simulation domain is updated. The time step of the particle motion simulation is usually shorter than the time difference between two consecutive electro-magnetic field data inputs. Therefore, the values of the electro-magnetic field adopted in the different time steps need to be determined, respectively. One solution is to employ a “step function” where the values of the electro-magnetic field remain unchanged in all time steps before loading a new external data input. Another solution is making an interpolation (e.g., linear interpolation) in time between the two consecutive input data to fill in the values for every time step. Generally, at each time step, the electro-magnetic field in the simulation domain is updated to adapt to varying externally input data. The differences between the results obtained using the two interpolation methods will be presented and discussed in Section 3.

Second, the test particles are launched into the simulation domain, through the Y-Z entrance surface plane at X = 19 R_E. The test particles are launched at different random initial positions on the entrance surface at each
time step during the simulation. The initial velocities of the launched test particles are represented by a Maxwellian distribution applied to all three components. A Maxwellian distribution is derived from the bulk velocity and temperature of the solar wind, where these parameters are determined from the MHD inputs and updated every time step. Therefore, a Maxwellian velocity distribution function for the injected particles is recomputed at each time step and injected particle velocity components are set up accordingly.

Third, all the particles inside the simulation domain are pushed under the influence of electro-magnetic field. Here all the particles include the particles newly launched in this time step, as well as the particles launched in previous time steps. The motion of the particles is calculated based on the electro-magnetic field. These particles are pushed to their new location, and the particles that move outside the simulation domain are removed from the simulation.

Finally, at each time step, we calculate the weight of test particles transformed into a lower charge state during the charge exchange reaction. In the simulation, the number of test particles is far less than the actual number of physical particles in the solar wind. Before launching the particles, we attribute to each test particle an initial weighting factor w(0), proportional to the number of physical particles, enabling the contribution of one test particle to represent hypothetical physical particles proportionally. The initial weight of each injected test particle can be calculated as:

$$w(0) = \frac{n_{SW} \cdot v_{SW} \cdot S \cdot \Delta t}{N_{\text{launch}}}, \qquad (5)$$

where $N_{\text{launch}}$ indicates the number of test particles launched each time step. Note that $n_{SW}$ and $v_{SW}$ indicate the solar wind density and velocity at the time of injection of the test particles, thus the numerical weight $w(0)$ is adapted at each time step to account for variations in the solar wind density and velocity. $S$ indicates the area of the entrance surface (Y-Z plane, $X = 19 R_E$), $\Delta t$ indicates the time step of the simulation. During a charge exchange reaction, a physical particle $X^{q+}$ transitions to a lower charge state ion $X^{(q-1)+}$ with a cascade of X-ray photons emitted. After each charge-exchange reaction the number of physical particles will be reduced, and therefore the weight $w$ of the corresponding test-particle will decrease in every time step. Therefore, the weight $w$ can be written as a function of time:

$$w(t) = w(t - \Delta t) \cdot (1 - e^{-\alpha_{X^{q+}, M} \cdot v_{X^{q+}} \cdot n_M \cdot \Delta t}). \qquad (6)$$

To conclude, at each time step, we will update the electro-magnetic field, launch new particles with an adapted initial weight $w(0)$ reflecting the solar wind conditions, push them together with old-ones, and then derive the X-ray emissions and evolve the weight $w(t)$. The continuous updating of the electro-magnetic field allows the simulation to adapt to time-dependent solar wind conditions. At any given time, we may extract the output results, and calculate the X-ray production of the lower charge state ion $X^{(q-1)+}$ for each test-particle per cell following the same calculation as in Xu et al. (2024):

$$q_{X^{(q-1)+}}(t) = \left[ \frac{X^{q+}}{O} \right] \cdot \left[ \frac{O}{H^+} \right] \cdot \sum_{\Delta E} Y_{X^{(q-1)+}} \cdot \frac{w(t)}{\Delta t \cdot \Delta V}, \qquad (7)$$

where $\sum_{\Delta E} Y_{X^{(q-1)+}}$ indicates the emission line energy combining all transitions in the energy range $\Delta E$, $\Delta V$ indicates the volume of the cell, $[X^{q+}/O]$ indicates the relative abundance of the ion species $X^{q+}$ with respect to oxygen O in the solar wind, and $[O/H^+]$ indicates the abundance of oxygen with respect to protons in the solar wind. The total X-ray emission for the particular ion species simulation is calculated by summing the X-ray production for the total number of test particles in all the cells.

Equations 6 and 7, for the weight of test-particles $w(t)$, and the X-ray production $q_{X^{(q-1)+}}(t)$ respectively, are identical to the steady-state version of the LaTeP model. Extensive details on the derivation of these equations may be found in Xu et al. (2024).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** The soft X-ray volume emissivity in the equatorial (a,c,e) and noon-midnight meridian (b,d,f) plane. (a), (b), (c), (d), and (e), (f) correspond to the results of $N_{\text{launch}} = 50,000, 150,000$, and $250,000$, respectively. (a1–a5) Indicate the results at 20–24 min, and the same for (b1–b5), (c1–c5), (d1–d5), (e1–e5), and (f1–f5). The white dashed circles indicate the inner boundary of Magnetohydrodynamics data cube located at $r = 3 R_E$.</figcaption></figure>

## 2.2. Benchmark for Number of Particles Launched Each Time Step

Due to the limitation of computational resources, it is not feasible to launch the same number of test particles as the physical particles in the solar wind. Therefore, we need to decide an appropriate number of test particles launched every time step as the benchmark. Too few test particles will lead to statistical low X-ray emission results, while too many test particles will cost huge amount of computational resources.

To benchmark the model, the simulations will be performed under a steady state electro-magnetic field. We perform 3 sets of steady state simulations for $O^{7+}$ with different number of particles launched every time step, each set of simulation will output X-ray emission results at 20, 21, 22, 23, and 24 min (real times). These 5 times are selected since from the results of our previous tests, there is no temporal evolution of the main structures of X-ray emissions after 20 min. The emission is obtained based on all spectral transitions for $O^{7+}$ in the SXI energy range (0.2–5 keV (Sembay et al., 2023)).

In the 3 sets of simulations, $N_{\text{launch}}$ is set to be 50,000, 150,000, and 250,000 particles, respectively. The external input data for these 3 sets of simulations are the same, which come from PPMLR MHD model (Hu et al., 2005) results under the solar wind conditions: $V_x = -400$ km s$^{-1}$, $V_y = V_z = 0$ km s$^{-1}$; $B_x = B_y = 0$ nT, $B_z = 10$ nT; $n_{\text{SW}} = 5$ cm$^{-3}$. The PPMLR MHD model provides electro-magnetic field at a spatial resolution of 0.2 $R_E$. For consistency with the D-LaTeP grid, we interpolate the fields from MHD model to a resolution of 0.05 $R_E$. To avoid the complexities associated with the plasmasphere and large MHD characteristic velocities from the strong magnetic field, the inner boundary of PPMLR MHD model is set to be 3 $R_E$. Therefore, the emission results in this study within the range of 3 $R_E$ need to be treated with extra caution.

We present the results in Figure 1. We can see that all these results are quite similar to each other. The general structure and position of the magnetosheath and cusps are not influenced by the number of particle launched every time step. However, with $N_{\text{launch}} = 50000$ (Figures 1a and 1b), dotted and pixelated patterns near the magnetopause are presented. A single pixelated dot may correspond to the X-ray produced by a small number of test particles. The number of launched particles is not large enough, thus the emission maps seem to be “coarse” and pixelated (Figures 2a1–a5 and b1–b5). As $N_{\text{launch}}$ increases, the dotted patterns in the image appear to be smoother (Figures 2a1, c1, e1 and b1, d1, f1).
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Coefficient of variation (CV) map in the equatorial (a)–(c) and noon-midnight meridian (e)–(f) plane. (a), (c), (b), (f), and (c), (g) correspond to the results of $N_{\text{launch}} = 50,000$, $150,000$, and $250,000$, respectively. (d) and (h) indicate the exact values of CV along the red lines in (a)–(c) and (e)–(f). The blue, orange, yellow line indicate the values for $N_{\text{launch}} = 50,000$, $150,000$, and $250,000$, respectively. The black dashed circles indicate the inner boundary of Magnetohydrodynamics data cube located at $r = 3 R_E$.</figcaption></figure>

Coefficient of variation (CV) is a measure of relative dispersion, defined as the ratio of a data set's standard deviation to its mean. To quantify the influence of different $N_{\text{launch}}$, we calculate the CV for the emissions at 20–24 min ($CV(x, y, z) = \frac{\sigma(Q(x, y, z, t))}{\langle Q(x, y, z, t) \rangle_r}$, where Q indicates the emissivity) for all 3 sets of simulations (Figure 2). The CV is low in the magnetosheath and cusps because there are more test particles in these regions, leading to higher statistics X-ray emissions. In the solar wind there are fewer particles distributed, which tends to increase the dispersion of the values. On the other hand, the emission in the solar wind is generally weak, causing the low mean value. These factors produce greater statistical fluctuations around the mean, making the relative deviation (i.e., CV) appear higher. And it is obvious that the value of CV decreases with increasing $N_{\text{launch}}$.

In Figures 2d and 2h we plot the profile of the CV along the red lines in the X and Y directions in Figures 2a, b, c and 2a, b, c respectively. In Figure 2h, the mean CV value in the magnetosheath region for $N_{\text{launch}} = 50,000$ (blue curve) is ~0.17, for $N_{\text{launch}} = 150,000$ (orange curve) is ~0.09, and for $N_{\text{launch}} = 150,000$ (yellow curve) is ~0.08. Obviously, when $N_{\text{launch}}$ increase, the values of CV become lower, leading to better simulation statistics. Indeed the performance of $N_{\text{launch}} = 250,000$ is better than the performance of $N_{\text{launch}} = 150,000$. However, the actual time to finish the simulations increases with $N_{\text{launch}}$ as well. For our computational platform (AMD EPYC 7452 32-Core, 64 CPU, 250Gb), it takes 25 CPUs and ~18, 78, and 129 hr and to finish the simulation of $N_{\text{launch}} = 50,000$, $150,000$, and $250,000$, respectively. The improvement of the performance cannot compensate the cost of the computational resources. Therefore, considering the balance between computational resource and quality of simulation results, we choose $N_{\text{launch}} = 150000$ as the benchmark for the number of particles launched each time step in this study.

## 2.3. Benchmark for Running Time of Simulation

After loading the first external input electro-magnetic field data, injected test particles require some time to move across all the regions in the simulation box. Therefore, before loading the subsequent electro-magnetic field to start the evolution of simulation, we should wait for particles to fill the simulation box and for the simulation to reach a steady state. The appropriate running time that allows the simulation to reach a steady state first needs to be determined as a benchmark, in order to not account it as a signature of time-dependent solar wind conditions.

As mentioned in the previous section, we have performed simulations that output X-ray emission results at 5 snapshot times from 20 to 24 min (i.e., 5 different running times). As indicated in Figure 1, the results from 20 to 24 min seem almost identical, which suggests the simulation already reaches a steady state by 20 min. The small differences among Figures 1a1–1a5 are due to the limited number of randomly launched particles. The randomly launched particles at different time step have different motions, resulting in slightly different X-ray emissions in this time series.
Figure 2 shows the CV map of the emission results at 20–24 min. As we have decided the benchmark of $N_{total}$ to be 150,000 previously, the low coefficient of variations for $N_{total}$ =150,000 (~0.09) indicates that the emissions vary slightly with time, and simulation has already reached a steady state by 20 min. Therefore, under the specific simulation domain used in this study, 20 min is reasonable as the benchmark for the simulation running time. We should first run the simulation under the influence of the first external input electro-magnetic field for 20 min, before loading another electro-magnetic field to start the evolution of the X-ray emission. Note that the benchmark for the simulation running time depends on the size of the simulation domain.

# 3. Simulation of Real-Time Solar Wind Events

## 3.1. Southward IMF Turning Case

To test the performance of D-LaTeP model under time-varying electro-magnetic field, we use the electro-magnetic field data derived from PPMLR MHD model for a real-time IMF southward turning event recorded by Wind on 17th, March 2015 from 07:25 UT to 07:55 UT. This IMF southward turning event has previously been investigated by Xu et al. (2022), where an analytical model describing the motion of the subsolar magnetopause was developed and applied to this event. The time-dependent solar wind parameters at $X = 19 R_E$ and the three dimensional E & B field data from MHD results are used as the input to D-LaTeP. The species of the test particles is $O^{7+}$. We use all spectral transitions for $O^{7+}$ in the SXI energy range to obtain the emission. Figures 3a–3c indicate the magnetic field, density $n$, bulk velocity $V_x$ component, and thermal velocity $V_{th}$ at $X = 19 R_E$. $B_x$, $B_y$ and $B_z$ are set to be 0. The initial recorded Wind data set spans from 07:25 UT to 07:55 UT, but in Figure 3 we plot the curves from $t = 0$ min, indicating the starting time of the simulation. Note the stationary solution corresponding to the condition at $t = 0$ min have been reached before the loading the subsequent time-varying electro-magnetic field. There is a strong IMF southward turning (from ~11 nT to ~−19 nT) at $t = 5$ min, while the density, bulk velocity, and thermal velocity are relatively stable. Please note that these solar wind conditions are applied at the entrance plane ($X = 19 R_E$) to launch test particles properly. It takes a few minutes for the particles to travel through the solar wind before any variations in the solar wind conditions can affect the X-ray emissions near the magnetopause.

The PPMLR MHD model provides electro-magnetic field at a spatial resolution of 0.2 $R_E$ with a cadence of 1 min. For compatibility with the D-LaTeP framework, we perform interpolation in both space and time: spatially interpolating the MHD fields to the D-LaTeP grid of 0.05 $R_E$, then temporally interpolating the fields to provide E & B at every D-LaTeP time step (0.05 s). There are two simple methods for the temporal interpolation, one is temporal linear interpolation, where values at each grid point and each time step change smoothly and gradually by using a linear interpolation between two consecutive inputs. The other method is a step function, where the input stays constant for every time step and only changes abruptly when the next input is loaded. Both approaches allow the model to have E & B value at every grid point at each time step, but they represent changes over time differently. These temporal interpolation procedures are also applied to the solar wind parameters at the entrance plane of the simulation box. Figures 3d–3i and 3j–3o indicate the simulated X-ray emission with linear interpolation and step function, respectively. Generally, the results of these two methods exhibit quite similar X-ray emissions and structures. The linear interpolation results are smoother, while there are some layered structure in the step function results as shown in Figures 3k, 3n, 3l, and 3o. These layered structures result from the sudden changes in the E & B field caused by the step function method. Therefore, the linear interpolation method performs smoother and better results. The results hereafter are all with the linear interpolation method.

Figures 3d–3g, 3e, 3h, and 3f, 3i indicate the X-ray emission with linear interpolation under the real-time IMF southward turning event at 0, 10, and 30 min, respectively. There are some small scale structures introduced by kinetic effect. Movie S1 in the shows the evolution of the X-ray emission near the magnetopause more comprehensively. We can clearly see the influence of the IMF southward turning on the X-ray emissions and the corresponding dynamic evolution.

In order to validate this evolution, we compare the results of the magnetopause standoff distance and the cusp latitude with the PPMLR MHD data cubes. We extract the subsolar magnetopause location from the last closed field line in the MHD data cubes, and compare with the location of the maximum emissivity from D-LaTeP results at the subsolar point every minute in Figure 4a. The movement of the magnetopause is equivalent between the two approaches.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** The solar wind conditions and D-LaTeP simulation results for the Interplanetary Magnetic Field southward turning case. (a–c) The magnetic field B<sub>x</sub>, B<sub>y</sub>, B<sub>z</sub>, component, density n, bulk velocity V<sub>x</sub> component, and thermal velocity V<sub>th</sub> at X = 19 R<sub>E</sub>. (d–i) And (j–o) indicate the simulated X-ray emissions with linear interpolation and step function, respectively. (d–f) And (g–i) indicate the X-ray emissions in the equatorial and noon-midnight plane, respectively. (d, g), (e, h), and (f, i) indicate the X-ray emissions at 0, 10, and 30 min, respectively. The distribution of (j–o) is identical to that of (d–i), but correspond to the step function results. The white dashed circles indicate the inner boundary of Magnetohydrodynamics data cube located at r = 3 R<sub>E</sub>.</figcaption></figure>

A similar comparison between the northern cusp latitude at r = 5 R<sub>E</sub> from the last closed field line in MHD data cubes and the emissivity at r = 5 R<sub>E</sub> shell in X-Z plane from D-LaTeP is performed in Figure 4b. Due to the limited size of simulation box in the tail region, a closed field line in the PPLMR data cubes appears as an open-close field line. As a consequence, it is difficult to determine accurately the last closed field line at the higher latitude cusp, thus only the boundary at lower latitude is displayed. The location of the emissions from D-LaTeP generally coincides well with the latitude of the cusps from MHD. However, one might notice that in 4(b) during 13–17 min, the emissions in D-LaTeP seem to move to higher latitude, while the cusps latitude seems to move to lower latitude. This is due to the particle nature during the reconnection in the test particle simulations. The particles are transported along with newly reconnected open field lines to the nightside, leading to the emissions appear at higher latitude. This reconstruction of the cusp is also shown in Movie S1. Therefore, the consistency between the two approaches validates our D-LaTeP simulations.

## 3.2. Dynamic Pressure Enhancement Case

In a second case scenario we use an event of dynamic pressure enhancement. The E & B field and solar wind parameters come from the PPMLR MHD simulation results for this event recorded by Wind between 14:22 UT and 14:52 UT. The test particles species is O<sup>7+</sup>. Figures 5a–5c indicates the magnetic field, density n, bulk velocity V<sub>x</sub> component, and thermal velocity V<sub>th</sub>. Similarly, B<sub>x</sub>, V<sub>y</sub> and V<sub>z</sub> are set to be 0, and t = 0 min indicates
<figure><img src="image_6.png" /><figcaption>**Figure 4.** The location of subsolar magnetopause and the northern cusp latitude for the Interplanetary Magnetic Field southward turning case. (a) The evolution of the subsolar magnetopause point derived from the last closed field line in the Magnetohydrodynamics (MHD) data cubes (red line) and maximum emissivity from D-LaTeP results (blue line). (b) A map of the local volume emissivity calculated with D-LaTeP, as a function of time and latitude extracted at a radius of $r = 5 R_E$. The volume emissivity colorscale units are $eVcm^{-3}s^{-1}$. The red line represents the cusp latitude extracted from the last closed field line in the Piecewise Parabolic Method with a Lagrangian Remap MHD data cubes.</figcaption></figure>

the starting time of the simulation. The magnetic field and velocity remain stable during this interval, while the density $n$ increases from ~16 $cm^{-3}$ to ~34 $cm^{-3}$, leading to an enhancement in the dynamic pressure by more than a factor of 2.

Figures 5d, 5e, 5f, 5g, 5h, and 5i indicate the X-ray emission with linear interpolation at 0, 10, and 30 min, respectively. We do not to run the step function for this case, since the results are smoother with the liner interpolation. It is clear that the emissions at the magnetopause are enhanced with the increase of the density. In addition, the magnetosphere is slightly compressed due to the enhanced dynamic pressure. Movie S2 shows the temporal evolution of the X-ray emission more comprehensively, in which we can clearly see dynamic evolution of the magnetopause and cusps. The asymmetry of the emission toward the +Y direction due to the large value of $B_y$ is also displayed.

Figure 6 shows the temporal evolution of the subsolar magnetopause and the northern cusp at $r = 5 R_E$ shell obtained from PPMLR MHD and D-LaTeP simulations. The motion of the subsolar magnetopause and the latitude of the northern cusp are consistent between the two approaches. In Figure 6b, the cusp latitude always aligns with the emissions, because there is no IMF southward turning in the dynamic pressure enhancement event, leading to a relatively stable structure of the cusps.

### 3.3. Imaging the X-Ray Intensity

To provide more information from the simulation aspect for the imaging missions (e.g., SMILE and Lunar Environment Heliospheric X-ray Imager (LEXI) (Branduardi-Raymont et al., 2018; Walsh et al., 2024)), we calculate the 5-min average of the intensity maps for the IMF southward turning case, which are obtained from virtual imagers set at (9.5, 0, 50) and (9.5, 50, 0) $R_E$ pointing along the -Z and -Y direction, respectively (hereafter referred to as integrated along the Z axis and Y axis), with a field-of-view of 30° × 30°. The schematic of the position and the field-of-view of the virtual imagers are presented in Figure S1 in Supporting Information S1. Realistic simulated observations may be achieved using the SXI simulation tool (Sembay et al., 2023) which is beyond the scope of this paper. The 5-min average can be considered as a rough estimation of the X-ray image observed by SMILE with 5-min exposure time. Figures 7c–7h indicates the 5-min average of intensity integrated
<figure><img src="image_7.png" /><figcaption>**Figure 5.** The solar wind conditions and D-LaTeP simulation results for the dynamic pressure enhancement case. (a–c) The magnetic field $B_x$, $B_y$, $B_z$, component, density $n$, bulk velocity $V_x$ component, and thermal velocity $V_{th}$ at $X = 19 R_E$. (d) and (e) indicate the X-ray emissions calculated with D-LaTeP, for linearly interpolated E&B Magnetohydrodynamics (MHD) field inputs at $t = 0$ min, in the equatorial and noon-midnight plane, respectively. And (f, g) and (h, i) indicate the results at $t = 10$ and $t = 30$ min, respectively. The white dashed circles indicate the inner boundary of MHD data cube located at $r = 3 R_E$.</figcaption></figure>

along Y axis for the time interval 0–5, 5–10, 10–15, 15–20, 20–25, and 25–30 min, respectively. These figures are smoother and the magnetic structures are less sharp (broader) than the emissivity profiles in the noon-midnight plane (Figures 3g–3i), because of the overlap of several emissivity layers that contribute to the total intensity map. Nevertheless, the evolution of the cusps and magnetopause is clearly displayed.

To better visualize the motion of the magnetopause, we derive values along the $\phi = 0^\circ$ line in the $\theta - \phi$ map, convert $\theta$ to X positions along X axis ($X = 50 R_E \cdot \tan \theta + 9.5 R_E$), then calculate the 5-min averaged intensity between 0 and 5, 5–10 and 10–15 min (Figure 7a) and between 15 and 20, 20–25, and 25–30 min (Figure 7b). The magnetopause first slightly moves outward while the intensity decreases in the first 15 min (Figure 7a), and then moves inward with intensity remaining nearly constant. This is in agreement with the evolution seen in the emissivity profiles in comparison with the subsolar magnetopause location extracted from the MHD last closed field lines in Figure 4a.

Figure 8 indicates the 5-min average of intensity integrated along Z axis, with the same panel order as in Figure 7. In Figures 8a and 8b, we plot the intensity along $\phi = 0^\circ$ line and convert to a function of X position along X axis.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** The location of subsolar magnetopause and the northern cusp latitude for the dynamic pressure enhancement case. The distribution of the panels is the same as in Figure 4.</figcaption></figure>

Once again, we can see the motion of both cusp regions and magnetopause. The location of subsolar magnetopause varies between ~6.5 to ~7.6 $R_E$. The magnetopause initially moves outward before moving inward. However, the cusps exhibit generally an opposite trend, first moving inward and then outward. This occurs because the magnetic field lines undergo reconnection and reconfiguration when the IMF southward turning arrives, and the cusps will eventually move to lower latitudes. This is again in agreement with the evolution seen in the emissivity profiles bordered by the MHD field closed lines in Figure 4b.

The 10-min average of intensity integrated along *Y* and *Z* axis for the IMF southward turning case is shown in Figure S2 in Supporting Information S1. The motion of the magnetopause and cusps is clearly displayed. Similarly, 10-min average of intensity integrated along *Y* and *Z* axis for the dynamic pressure enhancement case is shown in Figure S3 in Supporting Information S1. We can see the inward motion of the magnetopause and the enhancement of the X-ray intensity. To conclude, from Figures 7 and 8, Figure S2 and S3 in Supporting Information S1, the motion of the magnetopause and the cusps are clearly displayed in the 5-min and 10-min averaged intensity maps, suggesting the capability of the SMILE mission to detect the evolution of the magnetosphere under similar solar wind conditions with 5-min and 10-min exposure time. It should be noted that such diagnostics from SMILE will be more complex in practice, due to the orbit geometry and the pointing direction, which are not always aligned with the dipole axis as assumed in our projections. Moreover, significant image processing will be required to account for instrumental effects and sky background contamination.

**4. Discussion**

Several improvements are currently considered and will be tested in future studies. In this study, the D-LaTeP model is tested under two real-time cases. The values of density in these two real-time cases are relatively high, and the orientation of the SXI onboard SMILE is not necessarily aligned with the *Y* or *Z* axis. Therefore, it requires further investigation to see whether SMILE can effectively observe the motion of the magnetopause in weaker cases with current exposure time.

The simulation domain we adopt in this study is $0 \le X \le 19 \ R_E$, $-10 \le Y \le 10 \ R_E$, and $-10 \le Z \le 10 \ R_E$, which mainly concentrates on the dayside magnetopause region. It is feasible to expand the size of the simulation box to investigate the X-ray emissions at the flank magnetopause or even near the reconnection site at the magnetotail, and derive a more global view of the X-ray emission.
<figure><img src="image_9.png" /><figcaption>**Figure 7.** 5-Minute average of X-ray intensity integrated along *Y* axis for the Interplanetary Magnetic Field southward turning case. (c)–(h) Indicate the 5-min average of 0–5, 5–10, 10–15, 15–20, 20–25, and 25–30 min. The lines in (a) indicate the converted X positions along the line of *φ* = 0 line in the 5-min averaged intensity maps of 0–5 (blue), 5–10 (orange), and 10–15 min (yellow). The lines in (b) indicate the converted X positions along the line of *X* = 0 *R*<sub>E</sub> in the 5-min averaged intensity maps of 10–15 (yellow), 15–20 (purple), 20–25 (green), and 25–30 (cyan) minutes.</figcaption></figure>

As we have mentioned before, several studies point out that the exospheric neutral density can also be time-
dependent (Connor & Carter, 2019; Jung et al., 2022). The exospheric neutral density model we used in this study
is a quite simple equation (Equation 4), which cannot reflect the time varying neutral density. The Model for
Analyzing Terrestrial Exosphere model developed by Connor et al. (2024) can investigate storm-time exospheric
behaviors and derive the neutral density. Although we have not analyzed the effect of a more sophisticated neutral
density model on the D-LaTeP X-ray emission results, employing a time-dependent neutral density may better
reflect the reality of the highly dynamic Solar wind-Earth magnetosphere system.

**5. Conclusion**

In this study, we have developed a new D-LaTeP model based on the previous stationary LaTeP model. The major improvement is that the newly developed D-LaTeP model can be applied to time varying solar wind conditions. To determine the benchmark for number of particles launched at each time step and the benchmark for running time of simulation, we perform 3 sets of simulations with different number of particles launched every time step. Considering the balance between computational resource and quality of simulation results, N<sub>launch</sub> = 150000 is an appropriate number as the benchmark for the number of particles launched each time. Also, the results indicate that under the simulation domain used in this study, the simulation can reach a steady state at 20 min. Hence it is reasonable to use 20 min as the benchmark for the running time of the simulation.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** 5-Minute average of X-ray intensity integrated along Z axis for the Interplanetary Magnetic Field southward turning case. The distribution of the panels is the same as in Figure 7.</figcaption></figure>

To validate the D-LaTeP model under dynamic solar wind conditions, we perform simulation with the electro-magnetic field input from PPMLR MHD model for a real time IMF southward turning event and dynamic pressure enhancement event. The electro-magnetic field input from PPMLR MHD model is in 1-min time resolution. The 1-min resolution input needs to be interpolated in time to adapt the update of electro-magnetic field every time step. We compare the results of two different interpolation methods in time, the linear interpolation and the step function. The comparison indicates that the linear interpolation method performs better and smoother results. The results of the D-LaTeP model clearly exhibit the evolution of the X-ray emissions under both IMF southward turning and dynamic pressure enhancement event, indicating the robustness of the newly developed D-LaTeP model in dealing with dynamic solar wind conditions. We also compare the subsolar magnetopause point and the latitude of the cusps derived from MHD data cubes and D-LaTeP X-ray emissions. The location of the magnetopause and cusps in MHD and D-LaTeP are consistent, which validates our D-LaTeP simulations. To provide more information from the simulation aspect, we first calculate the intensity maps obtained from virtual imagers set at (9.5, 0, 50) and (9.5, 50, 0) $R_E$ pointing along the -Z and -Y direction, respectively, then calculate the 5-min and 10-min average of the intensity maps as a rough estimation of the X-ray images that may be obtained by SMILE with 5-min and 10-min exposure time. The motion of the magnetopause and cusps are clearly displayed in both 5-min and 10-min averaged results under an idealized projection, suggesting in principle the capability of the X-ray imaging missions (e.g., SMILE and LEXI) to detect the evolution of the magnetosphere under ideal observing conditions. In the next steps, our simulation results will be combined with the SXI instrument response functions and background noise to fully simulate future observations.
## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

Simulation results are available through the interactive catalog http://impex.latmos.ipsl.fr/LatHyS.htm. The simulation data used in the manuscript correspond to simulation ID D-LaTeP_PPMLR_IMFreve rsal_interp_15_07_25, D-LaTeP_PPMLR_IMFreversal_stepfun_15_07_25, and D-LaTeP_PPMLR_Pd yn_enhancement_15_07_25.

## Acknowledgments

This work was supported by CNES, through the Soleil-Heliosphere-Magnetosphere program. This study benefited from the IPSL Data and Computing Center ESPRI which is supported by CNRS, SU, CNES and Ecole Polytechnique and ANR TEMPETE project. Q. Xu is funded by the CSC-Université Paris-Saclay grant N° 202204910154.

## References

* Branduardi-Raymont, G., Wang, C., Escoubet, C. P., Adamovic, M., Agnolon, D., Berthomier, M., et al. (2018). Smile definition study report. (V1.0 ed.; Tech. Rep.). ESA/SCI. https://doi.org/10.5270/esa.smile definition study report-2018-12

* Connor, H. K., & Carter, J. A. (2019). Exospheric neutral hydrogen density at the nominal 10 r subsolar point deduced from xmm-newton x-ray observations. *Journal of Geophysical Research: Space Physics*, 124(3), 1612–1624. https://doi.org/10.1029/2018JA026187

* Connor, H. K., Jung, J., Qian, L., Sutton, E. K., Cucho-Padin, G., Crow, M., & Lee, S.-Y. (2024). Storm-time variability of terrestrial hydrogen exosphere: Kinetic simulation results. *Frontiers in Astronomy and Space Sciences*, 11, 1421196. https://doi.org/10.3389/fspas.2024.1421196

* Cravens, T. E. (1997). Comet hyakutake x-ray source: Charge transfer of solar wind heavy ions. *Geophysical Research Letters*, 24(1), 105–108. https://doi.org/10.1029/96GL03780

* Cravens, T. E., Robertson, I. P., & Snowden, S. L. (2001). Temporal variations of geocoronal and heliospheric x-ray emission associated with the solar wind interaction with neutrals. *Journal of Geophysical Research*, 106(A11), 24883–24892. https://doi.org/10.1029/2000JA000461

* Cumbee, R., Stancil, P., & Mcilvane, S. (2021). The interactive kronos charge exchange database. *Bulletin of the AAS*, 53(6).

* Dennerl, K., Lisse, C. M., Bhardwaj, A., Burwitz, V., Englhauser, J., Gunell, H., et al. (2006). First observation of Mars with xmm-newton - High resolution x-ray spectroscopy with rgs. *Astronomy and Astrophysics*, 451(2), 709–722. https://doi.org/10.1051/0004-6361:20054253

* Dunn, W. R., Weigt, D. M., Grodent, D., Yao, Z. H., May, D., Feigelman, K., et al. (2022). Jupiter's x-ray and uv dark polar region. *Geophysical Research Letters*, 49(11), e2021GL097390. https://doi.org/10.1029/2021GL097390

* Gong, Y., Sun, T., Tang, B., Guo, Y., Sembay, S., & Wang, C. (2025). Dynamic x-ray imaging of the magnetosheath expected during a super storm. *Frontiers in Astronomy and Space Sciences*, 12, 1563653. https://doi.org/10.3389/fspas.2025.1563653

* Hodges, R. R., Jr. (1994). Monte carlo simulation of the terrestrial hydrogen exosphere. *Journal of Geophysical Research*, 99(A12), 23229–23247. https://doi.org/10.1029/94JA02183

* Hu, Y. Q., Guo, X. C., Li, G. Q., Wang, C., & Zhao-Hui, H. (2005). Oscillation of quasi-steady earth's magnetosphere. *Chinese Physics Letters*, 22(10), 2723–2726. https://doi.org/10.1088/0256-307X/22/10/073

* Jung, J., Connor, H. K., Carter, J. A., Koutroumpa, D., Pagani, C., & Kuntz, K. D. (2022). Solar minimum exospheric neutral density near the subsolar magnetopause estimated from the xmm soft x-ray observations on 12 November 2008. *Journal of Geophysical Research: Space Physics*, 127(3), e2021JA029676. https://doi.org/10.1029/2021JA029676

* Kharchenko, V. (2005). Charge-exchange mechanism of x-ray emission. *AIP Conference Proceedings*, 774(1), 271–280. https://doi.org/10.1063/1.1960938

* Kharchenko, V., & Dalgarno, A. (2000). Spectra of cometary x rays induced by solar wind ions. *Journal of Geophysical Research*, 105(A8), 18351–18359. https://doi.org/10.1029/1999JA000203

* Kim, H., Connor, H. K., Jung, J., Walsh, B. M., Sibeck, D., Kuntz, K. D., et al. (2023). Estimating the subsolar magnetopause position from soft x-ray images using a low-pass image filter. *Earth and Planetary Physics*, 8(1), 173–183. https://doi.org/10.26464/epp2023069

* Koutroumpa, D., Lallement, R., Kharchenko, V., Dalgarno, A., Pepino, R., Izmodenov, V., & Quémerais, E. (2006). Charge-transfer induced EUV and soft x-ray emissions in the heliosphere. *Astronomy and Astrophysics*, 460(1), 289–300. https://doi.org/10.1051/0004-6361:20065250

* Koutroumpa, D., Modolo, R., Chanteur, G., Chaufray, J.-Y., Kharchenko, V., & Lallement, R. (2012). Solar wind charge exchange x-ray emission from Mars - Model and data comparison. *Astronomy and Astrophysics*, 545, A153. https://doi.org/10.1051/0004-6361/201219720

* Kuntz, K. D., Collado-Vega, Y. M., Collier, M. R., Connor, H. K., Cravens, T. E., Koutroumpa, D., et al. (2015). The solar wind charge-exchange production factor for hydrogen. *The Astrophysical Journal*, 808(2), 143. https://doi.org/10.1088/0004-637X/808/2/143

* Liang, G. Y., Sun, T. R., Lu, H. Y., Zhu, X. L., Wu, Y., Li, S. B., et al. (2023). X-ray morphology due to charge-exchange emissions used to study the global structure around Mars. *The Astrophysical Journal*, 943(2), 85. https://doi.org/10.3847/1538-4357/acac7e

* Momose, R., Matsumoto, Y., & Miyoshi, Y. (2025). Estimation of reconnection rate from soft x-ray emission at the earth's dayside magnetopause. *Geophysical Research Letters*, 52(12), e2024GL114342. https://doi.org/10.1029/2024GL114342

* Ringuette, R., Koutroumpa, D., Kuntz, K. D., Kaaret, P., Jahoda, K., LaRocca, D., et al. (2021). Halosat observations of heliospheric solar wind charge exchange. *The Astrophysical Journal*, 918(2), 41. https://doi.org/10.3847/1538-4357/ac0e33

* Ringuette, R., Kuntz, K. D., Koutroumpa, D., Kaaret, P., LaRocca, D., & Richardson, J. (2023). Observations of magnetospheric solar wind charge exchange. *The Astrophysical Journal*, 955(2), 139. https://doi.org/10.3847/1538-4357/acf3e2

* Robertson, I. P., Collier, M. R., Cravens, T. E., & Fok, M.-C. (2006). X-ray emission from the terrestrial magnetosheath including the cusps. *Journal of Geophysical Research*, 111(A12), A12105. https://doi.org/10.1029/2006JA011672

* Robertson, I. P., Sembay, S., Stubbs, T. J., Kuntz, K. D., Collier, M. R., Cravens, T. E., et al. (2009). Solar wind charge exchange observed through the lunar exosphere. *Geophysical Research Letters*, 36(21), L21102. https://doi.org/10.1029/2009GL040834

* Sembay, S., Alme, A. L., Agnolon, D., Arnold, T., Beardmore, A., Margeli, A. B. B., et al. (2023). The soft x-ray imager (sxi) on the smile mission. *Earth and Planetary Physics*. https://doi.org/10.26464/epp2023067

* Sibeck, D. G., Allen, R., Aryan, H., Bodewits, D., Brandt, P., Branduardi-Raymont, G., et al. (2018). Imaging plasma density structures in the soft x-rays generated by solar wind charge exchange with neutrals. *Space Science Reviews*, 214(4), 79. https://doi.org/10.1007/s11214-018-0504-7

* Sun, T., Wang, C., Sembay, S. F., Lopez, R. E., Escoubet, C. P., Branduardi-Raymont, G., et al. (2019). Soft x-ray imaging of the magnetosheath and cusps under different solar wind conditions: Mhd simulations. *Journal of Geophysical Research: Space Physics*, 124(4), 2435–2450. https://doi.org/10.1029/2018JA026093
Tkachenko, Y., Koutroumpa, D., Modolo, R., Connor, H., & Sembay, S. (2021). Charge exchange x-ray emission in the near-earth environment: Simulations in preparation for the smile magnetospheric mission. In *Sf2a-2021: Proceedings of the annual meeting of the french society of astronomy and astrophysics* (p. 487). SFA.

Walsh, B. M., Kuntz, K. D., Busk, S., Cameron, T., Chornay, D., Chuchra, A., et al. (2024). The lunar environment heliophysics x-ray imager (lexi) mission. *Space Science Reviews*, 220(4), 37. https://doi.org/10.1007/s11214-024-01063-4

Wang, C., & Branduardi-Raymont, G. (2018). Progress of solar wind magnetosphere ionosphere link explorer(smile) mission. *Chinese Journal of Space Science*, 38(5), 657-661. https://doi.org/10.11728/cjss2018.05.657

Wedlund, C. S., Behar, E., Nilsson, H., Alho, M., Kallio, E., Gunell, H., et al. (2020). Solar wind charge exchange in cometary atmospheres - Iii. Results from the rosetta mission to comet 67p/churyumov-gerasimenko (corrigendum). *Astronomy and Astrophysics*, 640, C3. https://doi.org/10.1051/0004-6361/201834881e

Whittaker, I. C., Sembay, S., Carter, J. A., Read, A. M., Milan, S. E., & Palmroth, M. (2016). Modeling the magnetospheric x-ray emission from solar wind charge exchange with verification from xmm-newton observations. *Journal of Geophysical Research: Space Physics*, 121(5), 4158-4179. https://doi.org/10.1002/2015JA022292

Xu, Q., Koutroumpa, D., Modolo, R., Sun, T., Connor, H., Sembay, S., & Tkachenko, Y. (2024). Modeling soft x-ray emissions at the dayside magnetopause. *Journal of Geophysical Research: Space Physics*, 129(8), e2024JA032687. https://doi.org/10.1029/2024JA032687

Xu, Q., Tang, B., Sun, T., Li, W., Zhang, X., Wei, F., et al. (2022). Modeling of the subsolar magnetopause motion under interplanetary magnetic field southward turning. *Space Weather*, 20(12), e2022SW003250. https://doi.org/10.1029/2022SW003250

Yang, Z., Jarvinen, R., Guo, X., Sun, T., Koutroumpa, D., Parks, G. K., et al. (2023). Deformations at earth's dayside magnetopause during quasi-radial imf conditions: Global kinetic simulations and soft x-ray imaging. *Earth and Planetary Physics*, 8(1), 59-69. https://doi.org/10.26464/epp2023059

Zhang, Y., Sun, T., Carter, J. A., Sembay, S., Koutroumpa, D., Ji, L., et al. (2023). Dynamical response of solar wind charge exchange soft x-ray emission in earth's magnetosphere to the solar wind proton flux. *The Astrophysical Journal*, 948(1), 69. https://doi.org/10.3847/1538-4357/a cc326

Zhang, Z., He, F., Zhang, X.-X., Liang, G., Wang, X., & Wei, Y. (2024). Solar wind charge-exchange x-ray emissions from the o5+ ions in the earth's magnetosheath. *Remote Sensing*, 16(9), 1480. https://doi.org/10.3390/rs16091480