# Automatic Identification of Past Radiation Belts Electron Enhancement Events Using a Ground-Based Parameter

Guillerme Bernoux, Gautier Nguyen, Angélica Sicard, Vincent Maget

► To cite this version:

Guillerme Bernoux, Gautier Nguyen, Angélica Sicard, Vincent Maget. Automatic Identification of Past Radiation Belts Electron Enhancement Events Using a Ground-Based Parameter. Earth and Space Science, 2025, 12 (4), 10.1029/2024ea003945. hal-05105015

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Distributed under a Creative Commons Attribution 4.0 International License
# Earth and Space Science

## METHOD

10.1029/2024EA003945

**Key Points:**

* We present a new method to automatically identify radiation belts electron enhancement events using ground-based measurements

* For post-1995 events, we also identify the most likely interplanetary driving cause using existing lists of solar wind and transient events

* The resulting list of events is now available and can be useful for conducting large statistical studies on space weather and space climate

**Supporting Information:**

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

**Correspondence to:**

G. Bernoux,
guillerme.bernoux@onera.fr

**Citation:**

Bernoux, G., Nguyen, G., Sicard, A., & Maget, V. (2025). Automatic identification of past radiation belts electron enhancement events using a ground-based parameter. *Earth and Space Science, 12*, e2024EA003945. https://doi.org/10.1029/2024EA003945

Received 30 AUG 2024
Accepted 9 JAN 2025

**Author Contributions:**

**Conceptualization:** Guillerme Bernoux, Gautier Nguyen, Vincent Maget

**Data curation:** Guillerme Bernoux, Gautier Nguyen, Angélica Sicard, Vincent Maget

**Formal analysis:** Guillerme Bernoux, Gautier Nguyen

**Funding acquisition:** Vincent Maget

**Investigation:** Guillerme Bernoux, Gautier Nguyen, Vincent Maget

**Methodology:** Guillerme Bernoux, Gautier Nguyen, Vincent Maget

**Project administration:** Vincent Maget

**Resources:** Angélica Sicard

**Software:** Guillerme Bernoux, Gautier Nguyen, Angélica Sicard

**Supervision:** Vincent Maget

© 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.

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## Automatic Identification of Past Radiation Belts Electron Enhancement Events Using a Ground-Based Parameter

Guillerme Bernoux¹ ID, Gautier Nguyen¹ ID, Angélica Sicard¹ ID, and Vincent Maget¹ ID

¹DPHY, ONERA, Université de Toulouse, Toulouse, France

**Abstract** We propose a new method for automatically identifying radiation belt electron enhancement events using Ca, a ground-based 1D surrogate index for the global dynamics of the electron radiation belts. Our method improves event identification by accurately determining start and end dates, considering intensification and return-to-equilibrium phases, and resolving ambiguity between single, longer-lasting events and multiple, successive events. The events detected after 1995, are automatically associated with possible physical driving causes, such as interplanetary coronal mass ejections and stream interaction regions. The resulting list of events is consistent with our understanding of how radiation belts respond to interplanetary drivers, in terms of event intensity, duration, and distribution over the solar cycle. The list of events from 1868 to 2021 is publicly available for statistical analyses of radiation belt climatology, constructing space weather forecasting models, and selecting periods of interest for studying physical processes.

**Plain Language Summary** The Earth is surrounded by regions filled with high-energy electrons and protons that can be trapped by the planet's magnetic field. These regions, called radiation belts, can be harmful to satellites and astronauts. We have developed a new way to automatically detect periods when these radiation belts have an increased number of high-energy electrons, which can last for days or weeks. Our method uses a simple index to determine when these events begin and end, and whether they are single events or multiple events occurring in succession. We also try to figure out what causes these events, such as solar storms or other interplanetary phenomena. Our new list of events from 1868 to 2021 is consistent with what scientists already know about how radiation belts respond to space weather, and is available to everyone for further analysis and research. This work should help the research community and industry to better understand and predict space weather and space climate, which can affect our daily lives through impacts on technology, communication systems, and navigation.

## 1. Introduction

The radiation belts are regions of the Earth's inner magnetosphere that contain high-energy electrons and protons, a large fraction of which are trapped. Their dynamics are partially driven by the solar wind and solar transients, including stream interaction regions (SIRs) and interplanetary coronal mass ejections (ICMEs). These structures can disturb the Earth's magnetosphere during events known as geomagnetic storms, leading to rapid changes, particularly increases, in particle fluxes in the radiation belts. Yet, periods of increased radiation belt electron fluxes can disrupt spacecraft operations, leading to temporary and permanent onboard anomalies (Choi et al., 2011; Iucci et al., 2005). Understanding the physical processes behind these events, accurately predicting them for safe operations, and characterizing them climatologically are crucial in designing more resilient spacecraft and missions.

However, there is no standard definition of these so-called radiation belts electron enhancement events (referred to as RB-events in this paper for conciseness). Studies that analyze a small number of events tend to identify them manually. This process usually takes into account flux or phase-space density data, combined with the authors expertise (e.g., Boyd et al., 2014; Li et al., 2014; Reeves et al., 2013). To statistically study a larger number of events in the radiation belts, some studies start with the definition of a geomagnetic storm or an interplanetary event and look at what follows in the radiation belts. This is typically done using geomagnetic indices, such as Dst (Sugiura, 1964), Kp (Matzka et al., 2021), or a set of solar wind parameters (see e.g. Benacquista et al., 2017; Kilpua et al., 2015; Tang et al., 2017; Zhao et al., 2019). However, geomagnetic indices are usually not sufficient to define RB-events since the response of the radiation belts to geomagnetic disturbances is difficult to model and assess (Reeves et al., 2003) and can sometimes lead to loss events (Herrera et al., 2016). Moreover, Reeves
**Validation:** Guillerme Bernoux,
Gautier Nguyen, Vincent Maget

**Visualization:** Guillerme Bernoux

**Writing – original draft:**
Guillerme Bernoux

**Writing – review & editing:**
Guillerme Bernoux, Gautier Nguyen,
Angélica Sicard, Vincent Maget

et al. (2020) points out that it would be interesting to rather start from a set of RB-events, and then study the conditions that led to them.

The direct use of in situ measurements therefore seems to be the most accurate solution, and definitions based on simple threshold exceedances are often used (see e.g. Meredith et al., 2015; Meredith et al., 2017). However, these definitions depend on various factors such as orbit, energy channels, instrument or spacecraft design. In addition, it is challenging to obtain a comprehensive list of past RB-events due to the short lifetime of space missions.

To address this issue, Reeves et al. (2020) use in situ data and carefully selects a threshold from a statistical analysis of the quantiles of the daily average flux value distribution, which enables the comparison of event lists obtained from different data sets. In addition, a duration criterion is added between two events: an event only ends if the average daily flux remains below the selected threshold for at least three consecutive days. This helps filter out minor fluctuations around the threshold that are not actual events. However, this method also prevents including events that occur in rapid succession before the flux value has fallen below the chosen threshold. In such cases, the method identifies these events as a single event with a cumulative duration equal to the duration of the successive events. In addition, their events begin when the fluxes have crossed a certain threshold, whereas an event can be considered to begin when the fluxes begin to increase, and thus a significant portion of the intensification phase of an event may be missed. This is all the more true when we consider the rarer events.

On the other hand, Bernoux and Maget (2020) propose a method that uses the *Ca* index, which was developed from ground values to serve as a 1D proxy for the filling state at the core of the outer electron belt for energies below ≈ 1 MeV (Rochel et al., 2016). The method is based on exceedances of *Ca* thresholds that depend on the current year and the global *Ca* distribution. Although using a ground proxy may not be as accurate as using in situ data, it does provide a long (longer than 150 years) and homogeneous event list with no data gaps. However, unlike the previous method, this one lacks additional criteria, thus including non-events that are minor fluctuations around the threshold. Additionally, it also does not allow for precise consideration of consecutive events.

At a time when data-driven statistical methods are proving their potential in heliophysics (Asensio Ramos et al., 2023) and space weather (Camporeale, 2019), it is crucial to have a comprehensive and consistent catalog of RB-events that distinguishes between successive events, facilitates comparison between two events, and, if possible, helps in understanding the underlying physical processes. In this paper, we build on the above-mentioned work and propose a new method for identifying RB-events using the *Ca* index, which addresses the issues highlighted above, while preserving their qualities. This results in an unprecedented, homogeneous, reproducible, and publicly available catalog of 4,243 events from 1868 to 2021 (without gaps), with fairly accurate start and end dates, which can be ranked in terms of relative intensity. In addition, our list is the first that identifies, for recent RB-events, their probable interplanetary cause.

We begin by describing the data sets we use in Section 2, before describing our new methodology in Section 3. In Section 4 we compare our results with past studies and highlight the added value of our approach. In conclusion, we discuss the benefits and shortcomings of our method and provide information about the publicly available online file containing the new list of events.

## 2. Data

We use two primary types of data. On the one hand, we use the *aa* and *Ca* indices to identify the RB-events, and on the other hand we use existing catalogs of solar wind and transient events to associate the possible driving cause of each RB-event.

### 2.1. The *aa* and *Ca* Indices

The *aa* index is a 3-hourly planetary geomagnetic index computed from two mid-latitude ground-based observatories (Mayaud, 1972). It is meant to describe the general state of disturbance of the Earth's magnetosphere on a planetary scale and has an uninterrupted ongoing 156-year history, dating back to 1868. *aa* is a K-derived geomagnetic index, meaning that it is made from combined measurements of the irregular variations of the horizontal component of the magnetic field. As such, it is directly related to the magnetic energy density. *aa* is measured in nanotesla. For further details, we refer the reader to (Menvielle et al., 2011). Quick-look values are made available in near-real time by the International Service of Geomagnetic Indices. However, definitive values are currently only available up to the end of 2021, so we only use data from 1868 to 2021
(inclusive), resulting in a data set spanning 154 years. aa can be used to characterize geomagnetic storms, but its correlation with electron flux measurements in the radiation belts is rather weak (Bernoux & Maget, 2020; Rochel et al., 2016).

That is why we mainly use the Ca index. Ca is a proxy index directly derived from the aa index with the following formula (Rochel et al., 2016):

$$Ca(t) = \frac{1}{\tau} \int_{0}^{\infty} aa(t-t') e^{-\frac{t}{\tau}} dt', \text{ with } \tau = 4 \text{ days.} \quad (1)$$

The convolution in time with an exponentially decreasing factor having a characteristic time of 4 days enables the consideration of energy accumulation in the radiation belts that may result from multiple successive events or longer-lasting events. It was shown in past studies (Bernoux et al., 2021; Bernoux & Maget, 2020; Rochel et al., 2016) that Ca correlates very well with electron radiation belt fluxes for medium-range energies (≈30 keV ≤ E ≤ 1 MeV) found at their core (≈3 ≤ L* ≤ 5). As Supplementary Information to this paper, we provide Figure S1, which shows the temporal dynamics of Ca and daily averaged electron fluxes for several energy ranges over the year 2017, allowing visual assessment of Ca's suitability for event identification across different energy levels. While there appear to be more lower energy events than higher energy events, Ca captures the overall dynamics quite well, with increased accuracy in the core of the domain, although it cannot capture all the subtleties of radiation belt dynamics. However, those previous studies demonstrated that it is a sufficient index to provide general information on the filling state of the radiation belts so that it can be used as a ground-based proxy measurement. Like aa, this index also has a 154-year history of definitive 3-hourly values, making it advantageous for statistical studies.

## 2.2. Lists of Interplanetary Events

To ensure exhaustiveness among the existing lists, we compiled a list of 811 events by aggregating the following ICMEs catalogs identified in WIND or ACE data between 1995 and 2022: Nguyen et al. (2019) and references therein, Möstl et al. (2017), Richardson and Cane (2010). For the SIRs, we applied the method presented in Grandin et al. (2019) on ACE data between 1995 and 2022 and aggregated those events to the catalog presented in Chi et al. (2018). The resulting SIR list is made of 1,137 events. For both ICMEs and SIRs, the possible duplicates were removed after a visual inspection of their most appropriate begin and end dates in OMNI data (King & Papitashvili, 2005).

## 2.3. Reference Lists of Radiation Belts Electron Enhancement Events

To enable comparisons with the existing literature, we use three reference lists of RB-events. The first list is the one by Bernoux and Maget (2020), which relies on straightforward, year-dependent, threshold exceedances of the Ca index. Their threshold is the larger of the average Ca and its halfway point between the average Ca and the current yearly mean (where average Ca is the mean value of Ca over the full period and the yearly mean is its mean value over the calendar year). For more details on the choice of such a threshold in their method, see Bernoux and Maget (2020).

The second and third reference lists are both based on the methodology developed by Reeves et al. (2020), but applied to two distinct data sets. We apply their method for finding the so-called moderate events (i.e., such that there are on average 100 events per 11-year period) to the time series of Ca values and to the time series of daily omnidirectional electron flux values (Bourdarie et al., 2012) for the 309–379 keV energy range measured by the RBSP-A/MagEIS instrument (Blake et al., 2013). We use these in situ measurements and specific energy channel as they are expected to correlate best with Ca, and therefore provide the most accurate direct comparison possible (which would not be the case if we used the GOES or LANL spacecraft data used in Reeves et al., 2020). Moreover, our catalog aims at identifying periods of interest for the study of in situ data, making such a comparison necessary.
# 3. Methods

## 3.1. RB-Event Identification Method

Our method for identifying RB-events is straightforward and relies mostly on threshold crossings, with additional steps to refine our list, establish more credible event start dates, and identify potential consecutive events mis-identified as a single event. We process as follows.

1. Provisional event identification:

(a) If the *Ca* index exceeds its mean value (19.5 nT), denote the provisional initiation of the event as *T*<sub>start</sub>.

(b) If the *Ca* index drops below its mean value, mark the provisional conclusion of the event as *T*<sub>end</sub>.

2. Event duration filtering:

* Exclude all events with a duration equal or less than 24 hr.

3. Event initiation adjustment:

(a) If the *aa* index exceeds its 95th percentile (59 nT) within a 48-hr window preceding *T*<sub>start</sub>, update the provisional start date accordingly.

(b) Ensure that the new start date does not overlap with the end date of the previous event by moving the end date of the previous event as far back as necessary.

(c) If the duration of the previous event has been reduced to less than 24 hr, the two events are merged.

4. Successive events identification:

* Define sub-events within each provisional event based on the following criteria:

(a) A sub-event may not begin less than 24 hr after the end of the previous event/sub-event and only if *Ca* has decreased in average for at least 24 hr.

(b) Initiate a sub-event when the *aa* index exceeds its 95th percentile and the *Ca* index continues to rise on average for at least 24 hr, only if there are at least 24 hr remaining in the provisional event.

(c) Conclude a sub-event when another sub-event starts, or when the provisional event ends.

Steps 1.a and 1.b are similar to the threshold-based methods used by Bernoux and Maget (2020) and Reeves et al. (2020). However, as illustrated in Section 4, following only these steps has two main consequences: including non-events that are mere fluctuations around the threshold, and being unable to distinguish successive events from a single longer-lasting one.

Step 2 addresses the first drawback by removing all potential events shorter than 24 hr, which are most likely anomalies.

Step 3.a adjusts the event start date by using the actual onset of the geomagnetic storm associated with the RB event as the event start time to account for the entire intensification phase. Steps 3.b and 3.c prevent overlapping RB-events, but it does not mean that each RB-event cannot have multiple overlapping driving causes.

Step 4 addresses the second problem by trying to identify potential sub-events within each RB-event that may have been missed. Criterion 4.a is used to avoid splitting a single multi-cause event into several separate RB-events. Criterion 4.b is used to identify the start of a sub-event from an identified geomagnetic storm. Criterion 4.c is again used to avoid overlapping RB-events. Note that sub-events are treated as individual events. It is possible to concatenate back together sub-events by identifying events that are contiguous in time.

The thresholds for *Ca*, *aa*, and duration, as well as the steps used in this method, were empirically chosen through a combination of educated guesses and comparison with maps of in situ measurements to assess their relevance. The obtained result is a good compromise between automation based on a 1D ground proxy and accuracy. This will be illustrated in Section 4. Our method finds a total of 4243 RB-events from 1868 to 2021.

## 3.2. Identification of Each Event's Most Likely Interplanetary Driving Cause

To determine the possible driving cause of each RB-event, we compare our obtained list to the aggregate interplanetary drivers list presented in Section 2.2. Out of the 710 RB-events found in the period 1995–2021, all but one had at least one interplanetary event occurring between the day prior to the event start time and the time at which *Ca* reached its maximum value. The exception is due to a lack of observational data at L1 at the time of the event. Using this observation, it is possible to sort the RB-events into five distinct categories that indicate their most probable physical origin.
1. Single SIR (333 events);

2. Single ICME (90 events);

3. Multiple SIRs (55 events);

4. Multiple ICMEs (46 events);

5. ICMEs and SIRs (185 events).

Seeing most RB-events being related to SIRs is not surprising as the associated catalogs contain more events that have longer duration, on average, than ICMEs. Most of the lists we used were manually compiled by experts. So, while the association between RB-events and interplanetary events is automated, it still needed human intervention and event-by-event analysis.

# 4. Results and Analysis

## 4.1. Comparison With Reference Lists of RB-Events

Figure 1 shows the results of our identification method for a 2-month period starting in September 2017, enabling a comparison between our list and reference lists. Our method identifies three distinct events at the beginning of September 2017 (panels 1.a and 1.b), while Bernoux and Maget (2020) method detected only one event that was the length of the three combined (panels 1.c and 1.d). Using both lists derived from Reeves et al. (2020), this period was divided into two events (panels 1.e, 1.f, 1.g, and 1.h). However, visual analysis of the in situ data suggests that there were actually three events, illustrating the benefits of using step 4 in our algorithm. This figure also demonstrates the ability of our method and that of Reeves et al. (2020) to filter out small fluctuations around the chosen threshold. In contrast, Bernoux and Maget (2020) method falsely identifies a short and weak event on 23 September 2017.

The lists also differ in start and end dates of events. Events in our list start closer to the beginning of the intensification phase thanks to step 3 of our method. Events in Reeves et al. (2020) lists start only after crossing the threshold of 100 events per cycle, resulting in a truncation of the intensification phase. The end dates also differ due to the different thresholds used for each method: the higher the threshold, the shorter the listed event. Using a lower threshold allows us to capture a larger part of the event's return-to-equilibrium phase. Depending on the user's needs, this may or may not be a desired characteristic. However, we believe it is more natural to define the start and end of an event as we do, ensuring that events are not abnormally short, like the one obtained with the Reeves et al. (2020) method applied on *Ca* around 2 September 2017 (panels 1.e and 1.f).

These differences are reflected in the statistical analysis of the lists. Figure 2 clearly shows that, we capture fewer short (Figure 2a.i) and weak (Figure 2b.i) (potentially false) events than Bernoux and Maget (2020). Conversely, our list contains fewer abnormally long events compared to other lists, thanks to the successive events search. For instance, during the RBSP-A era, the longest event in our list lasts approximately 14.5 days. In comparison, Bernoux and Maget (2020) recorded an event lasting around 23.5 days, and Reeves et al. (2020) method found events lasting 15.5 and 47 days when using *Ca* and RBSP-A/MagEIS data, respectively. When considering the full period from 1868 to 2021, the longest events last approximately 23 days, 73 days, and 130.5 days with our method, Bernoux and Maget (2020) method and Reeves et al. (2020) method when using *Ca*.

The use of a high threshold in the Reeves et al. (2020) method applied to *Ca* prevents it from finding the moderate (in terms of duration and intensity) events that our method does. However, the opposite is not true, as it appears that our method finds roughly the same number of higher-intensity events (Figure 2a.ii and Figure 2b.ii.). Our method and that of Reeves et al. (2020) applied to in situ data produce similar distributions, with our method finding proportionally more events with lower intensity (Figure 2a.iii and Figure 2b.iii.). This is mainly due to the fact that Reeves et al. (2020) method sets the number of events to be found (around 100 per 11-year cycle) before choosing the threshold. In contrast, we choose the threshold first and then rank events according to the maximum of *Ca*. An evidence for that is that our method finds 164 events in this period, as opposed to 73 events with their method. This approach enables us to identify weak events and filter them out if necessary. The search for successive events also allows our method to better identify medium events overshadowed by very intense events, which explains why we have proportionally more events of medium intensity.

There is, however, a difference between our methodology and that of the baselines regarding the definition of the end of an event. Indeed, according to the reference methodologies, an event ends when the chosen threshold is crossed downwards. When considering in situ measurements, and with an appropriate threshold, this can
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Comparison of the events identified in our list (a) and (b) with three reference lists: Bernoux and Maget (2020) (c) and (d), Reeves et al. (2020) method applied to *Ca* (e) and (f) and then applied to the daily omnidirectional electron fluxes for the 309–379 keV energy range observed by RBSP-A/MagEIS (g) and (h). Events from these lists are overlaid with *Ca* values from 2017-09-01 to 2017-11-03 in the panels on the left. A red dot indicates the maximum of each event. The dashed line indicates the *Ca* = 19.5 nT threshold used in our list. The dotted line indicates a threshold of *Ca* = 28.5 nT used by the Reeves et al. (2020) method applied to *Ca*. In the right panels, these same events are superimposed on the electron flux measurements mentioned above. L* denotes the Roederer (1970) parameter.</figcaption></figure>

correspond to either a return to a calm situation or at least to a low-risk situation for a mission, for example, With our method, an event can end in one of two ways: either the statistical threshold is crossed, or another event, with independent causes, begins before a return to quiet conditions. In the second case, despite the end of the event, we cannot always assume a strict return to quiet conditions. This is acceptable since our methodology is designed to identify and provide a list of past events that can be used for statistical studies and radiation belt modeling, not to be used in real time to assess risk to spacecraft with the highest accuracy.

## 4.2. Statistical Behavior of the RB-Events per Category

We show the yearly occurrence of the RB-events sorted in the categories defined in Section 3.2 on the top (a) panel of Figure 3. The yearly occurrence of RB-events, regardless of their categories, clearly follows an approximately 11-year cycle that lags behind the solar cycle (represented by the sunspot number) by around 2 or
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Count distribution of panel (a) events duration and (b) maximum *Ca* values, for our list compared to three reference lists: Bernoux and Maget (2020) (i.), the Reeves et al. (2020) method applied to *Ca* (ii.) and then applied to the daily omnidirectional electron fluxes for the 309–379 keV energy range observed by RBSP-A/MagEIS (iii.). All lists were restricted to the RBSP-A period of activity (2012-09-06–2019-10-13) for a fair comparison.</figcaption></figure>

3 years. This is consistent with the known dynamics of the Earth's outer electron radiation belts for these energy ranges (around $0.3 \le E \le 1$ MeV), as those are periods with an increased number of SIR-driven events with still a high amount of ICME-driven ones, and thus are periods of increased solar wind speed in average (Hudson et al., 2008; Miyoshi & Kataoka, 2011).

Additionally, for solar cycles 23 and 24, the frequency of ICME-related events increased during the years around the solar maxima periods. The same trend was observed for multiple ICME-driven events. Similarly, the SIR-driven events are more frequent in the declining phases of both solar cycles 23 and 24 and this is also particularly true for the multiple-SIR events that mostly occur in the years that come right after the solar maxima. This behavior is similar to that observed on the yearly occurrence of both ICMEs (Chi et al., 2016) and SIRs (Grandin et al., 2019), which is likely to be reflected in the dynamics of the radiation belts.

Looking now at the evolution of *Ca* during an RB-event, the bottom left (b) panel of Figure 3 represents the cumulative probability of the time required to reach the maximal value of *Ca* for each category. Having the cumulative probability of the ICME-related category on the top left corner of the panel indicates that a typical behavior of these events is a rapid reaching of the maximal value of *Ca*. On the other hand, the events related to multiple ICMEs appear to have a similar cumulative probability than the other categories. This is due to the accumulation of ICMEs before reaching the maximum rather than a slower increase in *Ca*. This also explains why the events related to multiple SIRs are the events that account for the largest time-to-max values and is consistent with the previous findings of Rochel et al. (2016).

Finally, the bottom right (c) panel of Figure 3 shows the cumulative probability of the difference between the maximal and the initial values of *Ca* for each category. The three categories that include ICMEs are below the one that only accounts for SIRs, suggesting that ICMEs tend to lead to higher jumps in *Ca*. This indicates that ICMEs, particularly multiple ones, are most disruptive in the radiation belts, in accordance with previous research (see e.g. Lugaz et al., 2017; Miyoshi & Kataoka, 2005). However, ICMEs and SIR may not disturb the same regions, with ICMEs being able to fill the slot region, and SIRs being able to cause extreme events in the outermost regions (Benacquista et al., 2018; Turner et al., 2019). This highlights a limitation in using a 1D proxy. To locate the region of intensification, further studies may consider combining the maximum *Ca* value with the interplanetary cause of an event.

Although very simple, the characterization of the RB-events per possible interplanetary cause exhibited statistical behaviors consistent with our knowledge on the influence of those transient events on the radiation belts. This paves the way for future deeper statistical analysis on the different categories of events.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Statistical analysis of events for which we have identified a possible interplanetary cause. Panel (a) shows the yearly occurrence of each category along with the 13-month smoothed sunspot number (dotted line). The bottom panels show (b) the cumulative probabilities of the time required to reach the peak value of *Ca* and (c) the difference between the maximum value of *Ca* and the value at event initiation for each category.</figcaption></figure>

**5. Description of the Events List File**

The event list file is made publicly available at https://doi.org/10.57745/W4HY2S (Bernoux et al., 2024). It is a CSV table with one header, 4,243 data rows and 6 columns.

1. *id*: the event's relative identifier;

2. *start_date*: event start date and time;

3. *end_date*: event end date and time;

4. *max_date*: date and time of maximum *Ca* of the event;

5. *max_value*: event's maximum *Ca* value (nT);

6. *category*: probable cause of the event, which can take six values: empty cell, “icme”, “sir”, “multiple_icmes”, “multiple_sirs”, “sir_and_icme”.
Date and times are in ISO 8601 format. We may update the list (and its formatting) in the future, for example, when new definitive values of the *aa* index are published.

**6. Discussion and Conclusions**

We proposed a new method for automatic radiation belt enhancement event identification. The method makes pioneering use of a ground proxy to ensure extensive, temporally homogeneous coverage without any gaps to provide the longest possible list of RB-events. Compared with previous works, it possesses other several advantages. It avoids falsely identifying slight fluctuations around the threshold value as events. Additionally, it facilitates distinguishing between successive events, preventing confusion with an excessively long single event. Moreover, the start and end dates of each event are chosen to capture as much of the intensification and return-to-equilibrium phases of each event as possible. Furthermore, including the maximum *Ca* value reached during each event provides an idea of their relative intensity, which, coupled with the list's length, makes it ideal for extreme event analysis. The methodology is reproducible and enables automatic identification of thousands of events within seconds.

This list of events may be useful for those conducting statistical studies of the Earth's radiation belts. It can be particularly helpful for superposed epoch analysis, data-intensive studies using machine learning methods, or in building or improving climatological models for satellite manufacturers. Physically, the presence of the causes of events will enable us to systematically study how radiation belts intensify following different types of disturbance. For example, many radiation belt models today rely on the use of *Kp* charts to quantify wave-particle interactions during storms. It is conceivable to refine these models by additionally differentiating the causes of events. This list of events will be used in the Horizon Europe "Forecast of Actionable Radiation Belt Scenarios" (FARBES) project, which aims to develop space weather models to monitor and predict the dynamics of radiation belts during an electron enhancement event using identified scenarios.

For studies that focus on precise analysis of one or more specific events, it is important to exercise caution when using this list. As it is automatic, we cannot guarantee that the start and end dates identified are the most accurate, nor that all events are perfectly identified. It is important to note that the association between RB-event and probable interplanetary driving cause is established through a simple cross-referencing of lists, rather than a precise and expert event-by-event analysis (even though most lists of ICMEs and SIRs were established manually by experts). Additionally, it should be noted that the *Ca* index is a good indicator of medium energy fluxes in the core of the electron belts. Therefore, our list may not be as accurate when analyzing events involving higher-energy electrons in geostationary orbit. However, even in this situation, our list can be valuable, for example, as a starting point for investigation, in order to identify potential periods of interest before expert analysis refines the events' boundaries or their driving cause.

**Data Availability Statement**

The resulting list of radiation belts electron enhancement events is available at the French national data repository
Recherche Data Gouv via https://doi.org/10.57745/W4HY2S with Etalab Open License 2.0 (Bernoux et al., 2024).

The *aa* index data are available from the International Service of Geomagnetic Indices via https://www.doi.org/
10.25577/9z05-v751 (Mayaud et al., 2023) with CC BY-NC license. The *Ca* index is a straightforward derivative
of the *aa* index (see Section 2.1). The RBSP-A/MagEIS data are publicly available at the Web site https://rbsp-ect.
newmexicoconsortium.org (Blake et al., 2013; Spence et al., 2013). The sunspot number data are available from
the World Data Center SILSO, Royal Observatory of Belgium, Brussels via http://www sidc.be/SILSO/ (SILSO
World Data Center, 1995) with CC BY-NC license.

**Acknowledgments**

The authors thank the FARBES project team and ONERA/DPHY/ERS team for relevant and useful discussions. The research leading to these results has received funding from the European Union's Horizon Europe research and innovation programme under Grant agreement 101081772.

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