in its essence, is based on image recognition of such plots. One recognizes each of specific footprints in such images corresponding to different regions. During a human inspection, only a small number of energies can generally be analyzed simultaneously. ML has no such limitation, and can analyze all the data simultaneously. Thus, a 3D image recognition is performed, in which a data cube composed of a stack of 32 (number of energy channels for MMS FPI Dual Ion Spectrometers (DIS) (Pollock et al. 2016)) angle-angle plots is analyzed. This approach is enabled by the homogeneous dataset provided by DIS in fast mode, i.e., the numbers of angular bins and the energy ranges are fixed (except for the special solar wind mode), and thus there is no need to reduce or resample the data.

The results for classification can be used in many different ways. One use is to define times when MMS is in a particular plasma region, for example, in a pristine magnetosheath (e.g., Svenningsson et al. 2023). Another use is to analyze the probabilities’ time series to identify the boundaries between the different plasma regions. For example, by analyzing the transition between the SW/IF and MSH, Lalti et al. (2022) identified ~3000 bow shock crossings by MMS. Similarly, magnetopause crossings can be identified from the transitions between the MSH and MSP classes. Another potential application is to use the probabilities (Fig. 29e) to quantify plasma mixing in boundary layers, which, for example, can occur in Kelvin-Helmholtz vortices (Settino et al. 2022). As a final remark, we note that the method as described is based solely on the classification of individual ion VDFs, and can be further extended to ingest the information on time evolution and/or other data (e.g., magnetic field, spacecraft position, etc.), which can enable an even more robust classification.

## Appendix B: The MMS "Quarter Moments" Data Product

The FPI on the MMS mission was designed to measure three-dimensional electron (ion) phase space densities every 30 (150) ms. This high data rate is achieved by arranging 8 electrostatic analyzers (ESA) for each species around the spacecraft, where the field-of-view of each ESA can be electrostatically deflected to four uniformly spaced look directions spanning 45 degrees. The high voltage power supply executes a full energy sweep (from ~10 eV to ~30 keV) for each of the four deflection states, so that the suite of analyzers simultaneously samples 8 uniformly spaced azimuthal angles for each energy sweep. This means that FPI samples a 32×16×8 regular (energy, zenith, azimuth) array every 7.5 (37.5) ms for electrons (ions), allowing for the possibility of recovering plasma moments a factor of 4 times faster than the nominal 30 (150) ms cadence. Figure 30 illustrates how the four deflection states are combined into the final FPI Level 2 phase space density "skymaps."

To recover 3D plasma moments at 7.5 (37.5) ms for electrons (ions), we use cubic spline interpolation to reconstruct 32 azimuthal samples – independently for each energy and zenith – from the 8 azimuthal samples corresponding to a single deflection state. To mitigate spline boundary condition issues, we interpolate an augmented set of data over the domain [-2π, 3π] in which data from [0, 2π] is copied to [-2π, 0] and [2π, 3π]. The interpolated data is then passed to the production moments algorithm as a (32 energy) × (16 zenith) × (32 azimuth) array.

Figure 31 shows a validation test in which 7.5 ms perpendicular electron bulk velocity data is compared to the E×B drift velocity (from the electric field and magnetometer experiments averaged down to 7.5 ms). The agreement is excellent, demonstrating that this is a simple, robust method for extracting accurate plasma moments. These "quarter moment" products have been used in many MMS publications (e.g., Phan et al. 2018)
<figure><img src="image_31.png" /><figcaption>**Fig. 30** The four FPI electrostatic deflection states (top four images), each composed of a full 32-step energy sweep and obtained at 7.5 ms cadence for electrons, are combined to produce a full Level 2 “skymap” (bottom left images) every 30 ms (adapted from Rager et al. 2018)</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>**Fig. 31** The spline interpolation technique does an excellent job of recovering electron bulk velocity at 7.5 ms resolution (green line) (adapted from Rager et al. 2018). The top three panels show three components of the electron convective electric field at 30 ms (red) and 7.5 ms (green) resolutions. The perpendicular electric field measured by the double probe instruments (Ergun et al. 2016; Lindqvist et al. 2016), averaged down to 7.5 ms, is shown in black. The bottom two panels show the electron pressures in the directions parallel and perpendicular to the magnetic field</figcaption></figure>

**Appendix C: Tables Summarizing Methods Reviewed in the Paper**
<table><caption>**Table 1 Methods for large-scale context**</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept, or name of data mining method</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>Maximum magnetic shear model</td><td>1-SC in solar wind</td><td>Time-of-flight analysis</td><td>ion moments (n, u<sub>j</sub>), B in solar wind</td><td>Magnetopause magnetic shear plot; dayside reconnection location</td><td>Entire magnetopause</td><td>T96 &amp; IMF draping models</td><td></td><td>Trattner et al. (2007); Kobel and Flückiger (1994); Tsyganenko (1995)</td></tr><tr><td>Global MHD model<sup>1</sup></td><td>1-SC in solar wind</td><td>MHD equations</td><td>MHD parameters in solar wind</td><td>MHD quantities everywhere in space and time</td><td>MHD</td><td>MHD</td><td></td><td>Tóth et al. (2005); see the CCMC website<sup>1</sup> for other codes</td></tr><tr><td>Data mining (DM) reconstruction<sup>2</sup></td><td>Multi-mission</td><td>DM using distance weighted kNN method and basis function magnetic field architectures</td><td>Magnetometer archives; Solar wind u<sub>sw</sub>, B<sub>z</sub>; geomagnetic indices SML, SMR &amp; their time derivatives</td><td>3D magnetic field parametrized by ~10<sup>3</sup> parameters derived from data</td><td>Magnetostatic</td><td>>1 R<sub>E</sub> and >5 min</td><td>Global activity indices availability</td><td>Stephens et al. (2019); Sitnov et al. (2008); Tsyganenko and Sitnov (2007)</td></tr><tr><td>Global in situ data reconstruction</td><td>Multi-mission</td><td>kNN statistical method</td><td>Solar wind parameters</td><td>3D global reconstruction of outer magnetosphere and magnetosheath properties</td><td>SW propagation from L1, magnetopause and bow shock models for normalizing data location</td><td>Dayside magnetosphere, magnetopause, and magnetosheath</td><td></td><td>Michotte de Welle et al. (2022)</td></tr></tbody></table>

<figure><img src="image_33.png" /></figure>

<footnote n="1">¹The code can be run at CCMC (https://ccmc.gscfc.nasa.gov/)</footnote>

<footnote n="2">²u<sub>sw</sub>: solar wind speed; B<sub>z</sub>: north-south component of the interplanetary magnetic field in geocentric solar magnetospheric (GSM) coordinates</footnote>
<table><caption>**Table 2** Methods for coordinate systems, frame velocity, and spacecraft trajectory estimation</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>References</th></tr></thead><tbody><tr><td>MVAB</td><td>1</td><td>∇ · <b>B</b> = 0</td><td><b>B</b></td><td>Variance directions; normal direction; LMN coordinates</td><td>1D structure to get a good estimate for the normal (σ<sub>N</sub>) direction</td><td>N/A</td><td>Sonnerup and Scheible (1998)</td></tr><tr><td>MVAe</td><td>1</td><td>None</td><td><b>u</b><sub>e</sub></td><td>Variance directions</td><td>Presence of electron outflow jets</td><td>Sub-ion scale</td><td>Genesteti et al. (2018)</td></tr><tr><td>MDDB</td><td>4</td><td>None</td><td><b>B</b>, SC position</td><td>Gradient directions; LMN coordinates</td><td>Steady structure</td><td>>SC separation</td><td>Shi et al. (2019)</td></tr><tr><td>Dimensionality</td><td>4</td><td>MDDB</td><td>MDDB eigenvalues</td><td>Dimensionality indices</td><td>Steady structure</td><td>>SC separation</td><td>Rezeau et al. (2018)</td></tr><tr><td>Hybrid method</td><td>4</td><td>MDDB + MVAB or MVAe</td><td><b>B</b>, SC position (and <b>u</b><sub>e</sub>)</td><td>LMN coordinates</td><td>Large maximum eigenvalues</td><td>>SC separation</td><td>Denton et al. (2018); Heuer et al. (2022)</td></tr><tr><td>HT analysis</td><td>1</td><td>∂<b>B</b>/∂t = 0</td><td><b>B</b>, <b>u</b> (or <b>E</b>)</td><td>Structure velocity</td><td>Not in diffusion regions</td><td>Ion or MHD scale</td><td>De Hoffmann and Teller (1950); Khrabrov and Sonnerup (1998)</td></tr><tr><td>MFR</td><td>1</td><td>Faraday's law, ∂<b>B</b>/∂t = 0</td><td><b>E</b> (or <b>u</b>), <b>B</b></td><td>Normal direction and velocity</td><td>1D structure, d/dt = 0, constant velocity</td><td>>SC separation</td><td>Dunlop and Woodward (1998)</td></tr><tr><td>4SC timing</td><td>4</td><td>None</td><td>Various</td><td>Normal direction and velocity</td><td>1D structure, d/dt = 0, constant velocity</td><td>>SC separation</td><td>Dunlop and Woodward (1998)</td></tr><tr><td>STD</td><td>4</td><td>B changes from convection</td><td><b>B</b>(t), SC position</td><td>Structure velocity</td><td>Partial time derivative=0</td><td>>SC separation</td><td>Shi et al. (2006, 2019)</td></tr><tr><td>Reconstructed X-line motion</td><td>4</td><td>∇ · <b>B</b> = 0; Ampère's law</td><td><b>B</b>, <b>j</b>, SC position</td><td>Velocity in reconnection plane</td><td>Various depending on model</td><td>Spacecraft spacing</td><td>Denton et al. (2021)</td></tr><tr><td>Comparison with simulation</td><td>1 or more</td><td>PIC code applicable</td><td>Various</td><td>Path through simulation domain</td><td>Simulation is realistic</td><td>Simulation scale</td><td>Shuster et al. (2017); Schroeder et al. (2022)</td></tr><tr><td>MCA</td><td>4</td><td>None</td><td><b>B</b>, SC position</td><td>B-variance eigenvectors; planarity; elongation; dimensionality</td><td>Steady structure</td><td>>SC separation</td><td>Fadanelli et al. (2019)</td></tr></tbody></table>

<figure><img src="image_34.png" /></figure>
<table><caption>**Table 3 Methods for reconstructing 2D/3D structures**</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>3D B-field quadratic reconstruction</td><td>4</td><td>Maxwell equations</td><td>B, j at 4 SC locations</td><td>Full 3D vector B-field near tetrahedron</td><td>No quadratic terms in minimum variance direction</td><td>Spacecraft spacing</td><td></td><td>Torbert et al. (2020)</td></tr><tr><td>3D polynomial reconstruction<sup>1</sup></td><td>4</td><td>∇·B = 0; Ampère's law</td><td>B, j at 4 SC locations</td><td>Full 3D vector B-field near tetrahedron</td><td>Various depending on model</td><td>Spacecraft spacing</td><td></td><td>Denton et al. (2020)</td></tr><tr><td>3D Polynomial Reconstruction with multiple input times<sup>2</sup></td><td>4</td><td>∇·B = 0; Ampère's law</td><td>B, j at 4 SC locations</td><td>Full 3D vector B-field near tetrahedron</td><td>Various depending on model</td><td>Spacecraft spacing</td><td></td><td>Denton et al. (2022)</td></tr><tr><td>3D empirical reconstruction using stochastic optimization method</td><td>4</td><td>Simultaneous perturbation stochastic approximation</td><td>B, j at 4 SC locations</td><td>Full 3D vector B-field near tetrahedron</td><td>∇·B = 0; Ampère's law</td><td>Spacecraft spacing</td><td></td><td>Zhu et al. (2022)</td></tr><tr><td>Simulation-assisted Multi-SC 2D reconstruction of an EDR</td><td></td><td>Comparison with kinetic simulation</td><td>Various</td><td>2D fields of all fluid quantities</td><td>Simulation is realistic</td><td>Ion skin depth</td><td></td><td>Schroeder et al. (2022)</td></tr><tr><td>Grad-Shafranov reconstruction</td><td>1</td><td>Static magnetic field, ∇·B = 0, Ampère's law</td><td>B, pressure</td><td>2D maps of B &amp; pressure</td><td>2D, MHD equilibrium</td><td>MHD scale</td><td>Predefined structure velocity</td><td>Sonnerup et al. (2006a,b)</td></tr><tr><td>MHD reconstruction</td><td>1</td><td>MHD equations</td><td>MHD parameters (occasionally incl. P-anisotropy)</td><td>2D maps of MHD quantities</td><td>2D, steady structure; various depending on model</td><td>MHD scale</td><td>Predefined structure velocity</td><td>Sonnerup et al. (2006a,b); Chen and Hau (2018); Teh (2019); Tian et al. (2020)</td></tr></tbody></table>

<figure><img src="image_35.png" /></figure>
<table><caption>**Table 3** (Continued)</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>EMHD reconstruction<sup>3</sup></td><td>1</td><td>EMHD equations</td><td>B, E, electron moments (n, u<sub>e</sub>, T<sub>e</sub>)</td><td>2D maps of B, E, and electron moments</td><td>2D, steady structure; various depending on model</td><td>Electron scale</td><td>Predefined structure velocity</td><td>Sonnerup et al. (2016); Hasegawa et al. (2021); Korovinskiy et al. (2021, 2023)</td></tr><tr><td>3D RBF-based reconstruction</td><td>Multi-SC</td><td>Euler potentials expressed by modified radial basis functions</td><td>B</td><td>Full 3D vector B-field near tetrahedron</td><td>Steady structure; ∇·B = 0</td><td>Spacecraft spacing</td><td></td><td>Chen et al. (2019)</td></tr></tbody></table>

<footnote n="1">¹The code is available at Zenodo (https://doi.org/10.5281/zenodo.3906853)</footnote>

<footnote n="2">²The code is available at Zenodo (https://doi.org/10.5281/zenodo.6395044)</footnote>

<footnote n="3">³The code is available at Zenodo (https://doi.org/10.5281/zenodo.5144478)</footnote>
<table><caption>**Table 4 Methods for diffusion region identification**</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept, or name of machine learning method</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th></tr></thead><tbody><tr><td>Electron-frame dissipation measure¹</td><td>1</td><td>j·(E + u<sub>e</sub>×B) – ρ<sub>e</sub> (u<sub>e</sub>·E)</td><td>j. E, B, u<sub>e</sub> [ρ<sub>e</sub>]</td><td>Scalar in units of [W/m³]</td><td>Maxwell's equations</td><td></td><td>Zenitani et al. (2011a)</td></tr><tr><td>Agyrotropy</td><td>1</td><td>Particle distribution function</td><td>P, b̂</td><td>Scalar</td><td>None</td><td>Diffusion region</td><td>Swisdak (2016)</td></tr><tr><td>Pressure strain</td><td>4</td><td>– (P·∇)·u</td><td>P, u</td><td>Ion and electron dissipation rate in scalar</td><td>Differentiable velocity; curlometer</td><td></td><td>Yang et al. (2017); Bandyopadhyay et al. (2021)</td></tr><tr><td>Electron vorticity</td><td>4</td><td>Ω<sub>e</sub> ~ ω<sub>e</sub> (electron gyrofrequency), defines the characteristic frequency of the EDR</td><td>v<sub>e</sub></td><td>Electron vorticity (Ω<sub>e</sub> = ∇ × u<sub>e</sub>)</td><td>None</td><td>EDR</td><td>Hwang et al. (2019)</td></tr><tr><td>Magnetic flux transport (MFT)²</td><td>1 or 4</td><td>∂<sub>τ</sub>ψ + U<sub>τ</sub>·∇⊥ψ = 0 (Advection equation of magnetic flux)</td><td>B, E, SC position</td><td>MFT velocity (U<sub>ψ</sub>) or its divergence (∇·U<sub>ψ</sub>)</td><td>Quasi-2D reconnection (k<sub>M</sub> ≪ k⊥); no magnetic field diffusion or generation</td><td>Diffusion region</td><td>Li et al. (2021); Qi et al. (2022)</td></tr></tbody></table>
<table><caption>**Table 4** (Continued)</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>Machine learning 1 to classify regions</td><td>1</td><td>feed-forward multilayer perceptron</td><td>Plasma and field quantities, reduced electron distribution functions</td><td>4 classes (IDR, EDR<sup>2</sup> boundary layers, magnetosphere)</td><td>None</td><td>IDR, EDR, boundary layers, magnetosphere</td><td></td><td>Lenouvel et al. (2021)</td></tr><tr><td>Machine learning 2 to identify EDRs</td><td>1</td><td>Convolutional Neural Network</td><td>Electron distribution functions</td><td>EDR / no EDR</td><td>None</td><td>Diffusion regions</td><td></td><td>Lenouvel (2022)</td></tr></tbody></table>

1 ρ<sub>c</sub>: electric change density

2 k<sub>M</sub>: wave number corresponding to the scale of the spatial variation in the direction of the reconnection X-line; k<sub>⊥</sub>: wave number corresponding to the scale of the spatial variation in the plane transverse to the X-line

<figure><img src="image_36.png" /></figure>
<table><caption>**Table 5** Methods for analyzing the electron diffusion region</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>Anomalous transport terms estimation from lower hybrid waves<fnref n="1" /></td><td>4</td><td>Fluid equations, wave-particle interactions</td><td><b>B</b>, <b>E</b>, particle moments (n, u, p)</td><td>Anomalous transport terms</td><td>None</td><td>Sub-ion scales</td><td>Particle moments that resolve the waves</td><td>Graham et al. (2022)</td></tr><tr><td>Computing ∂f<sub>e</sub>/∂t in 1 the spacecraft frame</td><td>1</td><td>Electron Vlasov equation</td><td>Electron distribution function f<sub>e</sub> at multiple times</td><td>∂f<sub>e</sub>/∂t (sometimes an approximation for ∂f<sub>e</sub>/∂N)</td><td>Cadence of electron measurements is sufficient for ∂f<sub>e</sub>/∂t estimation</td><td>Electron scale for EDR</td><td>Consult with the FPI team for proper implementation</td><td>(2019, Shuster et al. 2021a,b)</td></tr><tr><td>Computing the spatial gradient v·∇f<sub>e</sub> term</td><td>4</td><td>Electron Vlasov equation</td><td>f<sub>e</sub>, velocity-space coordinates for f<sub>e</sub>, SC position</td><td>∇v·∇f<sub>e</sub> and v·∇f<sub>e</sub></td><td>SC separation is sufficient for estimating ∇v·∇f<sub>e</sub></td><td>Electron scale for EDR</td><td>Consult with the FPI team for proper implementation</td><td>(2019, Shuster et al. 2021a,b)</td></tr><tr><td>Computing the F·∇v·∇f<sub>e</sub> term in the spacecraft frame</td><td>1</td><td>Electron Vlasov equation</td><td>f<sub>e</sub>, velocity-space coordinates for f<sub>e</sub>, E, B</td><td>∇v·f<sub>e</sub> and each of f<sub>e</sub>, the 3rd term of the Vlasov equation</td><td>Velocity-space resolution is sufficient for estimating ∇v·f<sub>e</sub></td><td>Electron scale for EDR</td><td>Consult with the FPI team for proper implementation</td><td>(2019, Shuster et al. 2021a,b)</td></tr><tr><td>Non-Maxwellianity<fnref n="1" /></td><td>1</td><td>Particle distribution function</td><td><b>B</b>, particle distribution, particle moments</td><td>Scalar non-Maxwellianity value</td><td>None</td><td>Kinetic scale of particles of interest</td><td>None</td><td>Greco et al. (2012); Servidio et al. (2017); Graham et al. (2021)</td></tr><tr><td>Kinetic entropy</td><td>1</td><td>Boltzmann's entropy theory</td><td>Particle distribution, particle moments</td><td>Entropy densities, scalar non-Maxwellianity value</td><td>None</td><td>Kinetic scale of particles of interest</td><td>Velocity space is properly resolved by particle measurements</td><td>Argall et al. (2022)</td></tr></tbody></table>

<figure><img src="image_37.png" /></figure>

<footnote n="1">¹The scripts and data required to reproduce the figures in Graham et al. (2022) can be found at https://zenodo.org/records/6370048, and the routines require the irfu-matlab software package: https://github.com/irfu/irfu-matlab</footnote>

<footnote n="1">¹The routines to compute non-Maxwellianity can be found at https://github.com/irfu/irfu-matlab</footnote>
<table><caption>**Table 6** Methods for reconnection electric field (EM) estimation</caption><thead><tr><th>Method</th><th># of space-craft (SC) needed</th><th>Underlying theory or concept</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>Direct measurement</td><td>1</td><td>Faraday's law</td><td><b>E</b></td><td><i>E</i><sub>M</sub></td><td>2D, steady</td><td>N/A</td><td>Accurate LMN</td><td>Genestreti et al. (2018); Burch et al. (2020)</td></tr><tr><td>Inflow velocity</td><td>1</td><td><b>u</b><sub>c</sub>, <b>n</b>, <b>B</b></td><td>Inflow velocity; normalized reconnection rate</td><td><i>E</i><sub>M</sub></td><td>2D, steady</td><td>EDR</td><td>Accurate LMN</td><td>Burch et al. (2020)</td></tr><tr><td>Multi-crescent VDF<sup>1</sup></td><td>1</td><td>Equations of motion</td><td><i>B</i><sub>L</sub>, <i>E</i><sub>N</sub>, <i>N</i>, <i>u</i><sub>c</sub><sub>M</sub></td><td><i>E</i><sub>M</sub></td><td><i>B</i><sub>L</sub>=b<i>N</i>, <i>E</i><sub>N</sub>=k<i>N</i>, No guide field</td><td>Electron scale</td><td><i>N</i>=0 crossing</td><td>Bessho et al. (2018)</td></tr><tr><td>Remote sensing at separatrix</td><td>More than 2</td><td>Magnetic flux conservation</td><td>Timing velocity at separatrix, <b>E</b>, <b>B</b></td><td><i>E</i><sub>M</sub></td><td>2D, constant reconnection rate, uniform field between multi-probes</td><td>Ion to MHD scale</td><td>Separatrix identification</td><td>Nakamura et al. (2018a)</td></tr><tr><td>Separatrix angle</td><td>1 or 4 (depending on inflow and outflow direction)</td><td>Flux and force balance along inflow and outflow direction</td><td><i>B</i><sub>N</sub>, <i>B</i><sub>M</sub> (or <i>B</i><sub>z</sub>, <i>B</i><sub>x</sub> for tail)</td><td>Normalized reconnection rate</td><td>2-D, low beta, separatrix angle=exhaust opening angle</td><td>Electron to ion scale</td><td>Separatrix identification</td><td>Liu et al. (2017); Nakamura et al. (2018b)</td></tr></tbody></table>

<figure><img src="image_38.png" /></figure>

<footnote n="1">¹N: distance in the normal (N) direction from the current sheet center ($B_L=0$) plane; see Sect. 3.3.1 for the definitions of b and k</footnote>
<table><caption>**Table 7 Methods for region and current sheet identification**</caption><thead><tr><th>Method</th><th># of spacecraft (SC) needed</th><th>Underlying theory, concept, or name of machine learning method</th><th>Input</th><th>Output</th><th>Assumptions</th><th>Spatial or temporal scale of interest</th><th>Other requirements if any</th><th>References</th></tr></thead><tbody><tr><td>ML-based region identification<sup>1</sup></td><td>1</td><td>Gradient boosting/machine learning</td><td>B, ion moments (n, u, T<sub>i</sub>)</td><td>Near-Earth region labelling, magnetopause and bow shock crossings</td><td>None</td><td>MHD<sup>2</sup> and larger</td><td>N/A</td><td>Nguyen et al. (2022a)</td></tr><tr><td>Region classification using 3D particle distributions</td><td>1</td><td>Convolutional Neural Network/supervised machine learning</td><td>3D ion velocity distributions</td><td>Near-Earth region labelling, magnetopause and bow shock crossings</td><td>None</td><td>MHD and larger</td><td>N/A</td><td>Olshevsky et al. (2021)</td></tr></tbody></table>

<footnote n="1">¹The code is available at github (https://github.com/gautiernguyen/in-situ_Events_lists)</footnote>

<footnote n="1">¹Magnetohydrodynamics</footnote>
**Acknowledgements** We thank the reviewers whose comments helped improve this manuscript. The work by H.H. was supported by JSPS Grant-in-aid for Scientific Research KAKENHI 21K03504. Part of the work done while H.H. was at SwRI was supported by NASA Contract No. NNG04EB99C at SwRI. M.R.A. acknowledges support from NASA grant 80NSSC23K0409 and NSF award 2308670. R.E.D. was supported by a NASA grant 80NSSC22K1109. The work by D.B.K. has been supported by the Austrian Science Fund (FWF): I 3506-N27 and Austrian FFG project ASAP15/873685. T.C.L. was supported by NSF award AGS-2000222. T.K.M.N. was supported by the Austrian Research Fund (FWF) P32175-N27. S.M.P was supported by Contract 499935Q. Y.Q. was supported by NASA MMS mission NNG04EB99C. The authors acknowledge the International Space Science Institute in Bern for supporting the international team of the workshop "Magnetic Reconnection: Explosive Energy Conversion in Space Plasmas".

**Funding** Open access funding provided by Uppsala University.

**Declarations**

**Competing Interests** The authors declare they have no conflicts of interest.

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## Authors and Affiliations

H. Hasegawa¹ ID · M.R. Argall² · N. Aunai³ · R. Bandyopadhyay⁴ · N. Bessho⁵,⁶ ·

I.J. Cohen⁷ · R.E. Denton⁸ · J.C. Dorelli⁶ · J. Egedal⁹ · S.A. Fuselier¹⁰,¹¹ · P. Garnier¹² ·

V. Génot¹² · D.B. Graham¹³ ID · K.J. Hwang¹⁰ · Y.V. Khotyaintsev¹³,¹⁴ ID ·

D.B. Korovinskiy¹⁵ · B. Lavraud¹²,¹⁶ · Q. Lenouvel¹² · T.C. Li⁸ · Y.-H. Liu⁸ ·

B. Michotte de Welle³ · T.K.M. Nakamura¹⁵,¹⁷ · D.S. Payne¹⁸ · S.M. Petrinec¹⁹ · Y. Qi²⁰ ·

A.C. Rager⁶ · P.H. Reiff²¹ · J.M. Schroeder⁹ · J.R. Shuster² · M.I. Sitnov⁷ · G.K. Stephens⁷ ·

M. Swisdak¹⁸ · A.M. Tian²² · R.B. Torbert²³,²⁴ · K.J. Trattner²⁰ · S. Zenitani¹⁵,²⁵

H. Hasegawa
hase@stp.isas.jaxa.jp

D.B. Graham
dgraham@irfu.se

Y.V. Khotyaintsev
Yuri.Khotyaintsev@rymdfysik.uu.se

¹ Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Sagamihara, Kanagawa 252-5210, Japan

² Space Science Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA

³ CNRS, Ecole polytechnique, Sorbonne Université, Université Paris Sud, Observatoire de Paris, Institut Polytechnique de Paris, Université Paris-Saclay, PSL Research University, Laboratoire de Physique des Plasmas, Palaiseau, France

⁴ Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA

⁵ Department of Astronomy, University of Maryland, College Park, MD 20742, USA

⁶ Heliophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

⁷ Applied Physics Laboratory, The Johns Hopkins University, Laurel, MD, USA

⁸ Department of Physics and Astronomy, Dartmouth College, Hanover, NH, USA

⁹ Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA

¹⁰ Southwest Research Institute, San Antonio, TX, USA

¹¹ University of Texas at San Antonio, San Antonio, TX, USA

¹² Institut de Recherche en Astrophysique et Planétologie, CNRS, Université Paul Sabatier, CNES, Toulouse, France

¹³ Swedish Institute of Space Physics, Uppsala, Sweden

¹⁴ Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden

¹⁵ Space Research Institute, Austrian Academy of Sciences, Graz, Austria
16 Laboratoire d'Astrophysique de Bordeaux, Université Bordeaux, CNRS, Pessac, France

17 Krimgen LLC, Hiroshima 732-0828, Japan

18 Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, USA

19 Lockheed Martin ATC, Palo Alto, CA, USA

20 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO, USA

21 Rice Space Institute, Rice University, Houston, TX, USA

22 Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, School of Space Science and Physics, Institute of Space Sciences, Shandong University, Weihai, Shandong 264209, People's Republic of China

23 Southwest Research Institute, Durham, NH, USA

24 Physics Department, University of New Hampshire, Durham, NH, USA

25 Research Center for Urban Safety and Security, Kobe University, Kobe 657-8501, Japan