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A Catalog of Automatically Identified Multi-Signature ICMEs Observed by Solar Orbiter

This study presents a catalog of 138 multi-signature interplanetary coronal mass ejections observed by Solar Orbiter, characterizing their distinct physical properties, expansion dynamics, and heavy-ion composition to establish a robust benchmark for space-weather research and CME evolution modeling.

Original authors: Felix N. Minta, Stefano A. Livi, George C. Ho, Susan T. Lepri, Heather A. Elliott

Published 2026-07-07

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CC BY 4.0

Original authors: Felix N. Minta, Stefano A. Livi, George C. Ho, Susan T. Lepri, Heather A. Elliott

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the Sun as a giant, active lighthouse that occasionally sneezes massive clouds of hot gas and magnetic energy into space. These sneezes are called Coronal Mass Ejections (CMEs). When they travel through the space between the planets, they are known as ICMEs (Interplanetary Coronal Mass Ejections).

This paper is essentially a high-tech "Wanted Poster" catalog created by scientists using data from a spacecraft called Solar Orbiter. Their goal was to automatically find and list these sneezes without needing a human to stare at graphs all day.

Here is a breakdown of what they did and found, using simple analogies:

1. The Problem: Finding a Needle in a Haystack

For years, scientists had to manually look at data to find these ICMEs. It was like trying to find a specific type of cloud in the sky just by looking at it with your eyes. Different people saw different things, leading to disagreements. Some catalogs missed weak sneezes, while others counted things that weren't sneezes at all.

The Solution: The team built a robot detective (an algorithm). Instead of guessing, this robot looks for a specific "fingerprint" made of multiple clues:

Magnetic Field: Is the magnetic force stronger than usual?
Temperature: Is the gas surprisingly cold (even though it's in space)?
Speed: Is the gas slowing down in a specific way?
Density: Is the gas packed tighter than normal?

If the robot sees at least three of these clues happening at once, it flags the event as an ICME.

2. The Three Types of "Sneezes"

The robot didn't just find one kind of sneeze; it sorted them into three categories, like sorting mail:

The "Good" Magnetic Clouds (Good-MC): These are the perfect sneezes. They have a smooth, organized magnetic twist (like a rope) and are very cold inside. Think of these as a neatly wrapped gift.
The "Complex" Clouds: These are messy. They have some magnetic twist, but the gas inside is hotter and the structure is a bit jumbled. It's like a gift that got crushed in the mail.
The "Non-MC" Clouds: These are sneezes that don't have the neat magnetic rope structure at all. They are just blobs of gas that are moving fast and are a bit cooler than the surrounding space. These are the hardest to spot, but the robot found them anyway.

3. The "Shockwave" and the "Traffic Jam"

About 55% of the sneezes the robot found had a special companion: a shockwave and a sheath.

The Shock: Imagine a supersonic jet breaking the sound barrier. That creates a loud boom. In space, a fast sneeze creates a shockwave in front of it.
The Sheath: This is the "traffic jam" of gas and magnetic fields squeezed between the shockwave and the sneeze itself. It's turbulent and hot.

The paper found that sneezes with this "traffic jam" (sheath) are much more intense. They are:

Longer: They last longer.
Stronger: They have more magnetic power.
Faster: They move quicker.
Hotter: The gas inside is much hotter.
Denser: They are packed with more particles.

In short, a sneeze with a shockwave is like a freight train, while a sneeze without one is more like a bicycle.

4. The "Chemical Fingerprint" Check

To make sure their robot wasn't making mistakes, the scientists checked the "chemical makeup" of the gas in 32 of these events. They looked at heavy ions (like Iron and Oxygen) to see if they matched what we expect from the Sun's atmosphere.

The Oxygen Test: They checked the ratio of two types of Oxygen. Surprisingly, this test only worked for about 30% of the sneezes. It's like trying to identify a person by their shoe size; it doesn't always work.
The Iron Test: They checked the "charge" of Iron atoms. This worked much better, identifying 50–65% of the sneezes. This suggests that many of these sneezes come from very hot parts of the Sun.

5. The Big Picture

The scientists tracked these events from 2020 to 2025. They noticed that the number of sneezes is rising, just like the number of sunspots (which is the Sun's way of showing it's getting more active).

Quiet Times (2020): Very few sneezes.
Busy Times (2023–2024): The number of sneezes, especially the fast ones with shockwaves, is peaking.

Why This Matters

This paper gives us a clean, reliable list of 138 solar sneezes. Because the list was made by a robot using strict rules, other scientists can trust it. It helps us understand:

How these sneezes grow and change as they travel away from the Sun.
Which ones are likely to cause "space weather" (like auroras or disruptions to satellites) when they hit Earth.
How the Sun's atmosphere behaves during its active phases.

In summary, the authors built a smart tool to automatically catch solar sneezes, sorted them by how "messy" or "organized" they are, and proved that the ones with shockwaves are the most powerful and dangerous.

Problem Statement

Interplanetary Coronal Mass Ejections (ICMEs) are critical drivers of space weather, yet their identification remains challenging due to the diversity of their in-situ signatures and the lack of a universal detection criterion. Historically, ICME catalogs have been compiled through expert-driven, visual inspection of magnetic and plasma data. This manual approach introduces subjectivity, inter-observer variability, and significant discrepancies between catalogs (e.g., Richardson 2014 found only 36% overlap between catalogs using the same Ulysses dataset). Furthermore, many existing catalogs rely heavily on magnetic field signatures alone, potentially biasing detection toward Magnetic Clouds (MCs) while missing non-MC ICMEs or events with weak magnetic rotations. Additionally, the lack of automated, reproducible methods hinders the creation of reliable labeled datasets required for machine learning applications and the development of data-driven CME evolution models.

Methodology

The authors developed an automated ICME detection algorithm utilizing high-resolution in-situ measurements from the Solar Orbiter mission (January 2020 – February 2025). The methodology is built on three core pillars:

Distance-Normalization: To account for Solar Orbiter's wide orbital range (0.3–1 au), the algorithm normalizes plasma and magnetic field parameters to 1 au. Using power-law fits derived from the data (e.g., $n_p \sim r^{-1.9}$ , $T_p \sim r^{-0.7}$ ), the authors remove background radial trends, allowing for uniform detection thresholds across the inner heliosphere.
Sliding Window Multi-Signature Detection: The algorithm scans data using a 36-hour sliding window (stepped by 1 hour) to identify candidate intervals.
- Pre-filters: Candidates must exhibit enhanced magnetic fields ( $\ge 7$ nT) and low plasma $\beta$ ( $< 0.8$ ).
- Non-MC ICMEs: Identified if at least three of six signatures are present: enhanced magnetic field, anomalously low proton temperature ( $T_{norm}/T_{exp} < 0.5$ ), low normalized plasma $\beta$ ( $\le 0.1$ ), declining speed profile, significant magnetic field direction change ( $\ge 45^\circ$ ), or low magnetic field variance.
- Magnetic Clouds (MCs): Identified via smooth, large-scale magnetic field rotation. The algorithm uses quadratic and linear regression to assess field smoothness ( $R^2$ ) and magnitude changes ( $\Delta B$ ). Events are classified as "Good-MC" (strict criteria) or "Complex flux rope" (relaxed criteria for disordered structures).
- Shocks and Sheaths: Shocks are detected via abrupt jumps in magnetic field ( $|B|_{down}/|B|_{up} \ge 1.2$ ), density, and velocity ( $\Delta V \ge 20$ km s $^{-1}$ ). Sheaths are defined as the turbulent region between the shock and the ICME leading edge, confirmed by high plasma $\beta$ , elevated density/temperature, and high variance.
Hierarchical Merging: Overlapping candidate intervals are merged based on a hierarchy of signature quality (e.g., a "Good-MC" overrides a "Complex flux rope") to produce a clean, non-redundant catalog.
Validation: The catalog was validated against external lists (ICMECAT and LASSOS) and cross-referenced with Heavy Ion Sensor (HIS) composition data (available Jan 2022–Apr 2023) to verify heavy-ion signatures (e.g., $O^{7+}/O^{6+}$ , $\langle Q_{Fe} \rangle$ ).

Key Contributions

A Comprehensive Catalog: The study presents a catalog of 138 ICMEs observed by Solar Orbiter, comprising 55% shock-associated events (with sheaths) and 45% non-shock events.
Substructure Resolution: Unlike many previous catalogs, this work explicitly identifies and characterizes three distinct substructures: forward shocks, turbulent sheaths, and the ejecta (MC or non-MC).
Automated Framework: The paper provides a fully automated, distance-invariant framework that minimizes subjectivity, enabling continuous updates and facilitating the creation of datasets for machine learning.
Composition Integration: The catalog integrates heavy-ion composition data for a subset of events, linking in-situ plasma properties to coronal source conditions.

Key Results

Radial Evolution: The study confirms that proton temperature decreases sub-adiabatically with distance, density follows a near-spherical radial decrease ( $n_p \propto r^{-1.9}$ ), and dynamic pressure drops steeply, indicating stronger ICME expansion in the inner heliosphere. The magnetic field radial dependence is shallower than the Parker spiral expectation.
Sheath vs. Non-Sheath Dynamics:
- ICMEs with sheaths are, on average, 27% longer, 68% more magnetized, 30% faster, 2.6 times hotter, 34% denser, and 2.8 times more over-pressured than those without sheaths.
- Sheath-associated events expand 96% faster.
- A strong correlation exists between ICME expansion speed and both size and speed for sheath events, suggesting sheath dynamics sustain over-pressured ejecta.
Solar Cycle Correlation: The frequency of ICMEs tracks the rise of Solar Cycle 25 (correlation coefficient $\approx 0.73$ with sunspot numbers). Shock-associated ICMEs peak later (2024) than non-shocked events (2023), consistent with faster CMEs driving shocks at activity maximum.
Composition Signatures:
- For the 32 events with HIS data, only $\approx 30\%$ satisfied the $O^{7+}/O^{6+} \approx 1$ criterion, indicating this ratio is not a universal identifier during the solar minimum/rising phase.
- In contrast, Fe-based indicators ( $\langle Q_{Fe} \rangle$ , $Fe^{16+}/Fe$ , Fe/O) identified 50–65% of the ICMEs, suggesting they are more robust markers for "hot" ICMEs originating from regions with high coronal electron temperatures.
- Superposed epoch analysis confirms that heavy-ion signatures are enhanced specifically within the magnetic cloud intervals, validating the algorithm's boundary detection.

Significance and Claims

The authors claim that this catalog provides a high-fidelity, substructure-resolved dataset that bridges the gap between in-situ observations and CME source conditions. By moving away from manual, magnetic-only identification, the study offers a physically grounded framework that:

Reduces Subjectivity: Provides a reproducible method for ICME identification, addressing the inconsistencies found in expert-driven catalogs.
Enables Advanced Modeling: The consistent, labeled dataset is suitable for machine-learning applications and data-driven models of CME evolution.
Connects Dynamics to Composition: The integration of heavy-ion data with kinematic profiles allows for a better understanding of how coronal heating and ionization conditions translate to in-situ ICME properties.
Facilitates Multi-Mission Analysis: The distance-normalized approach enables direct comparison with established 1-au catalogs and supports the creation of multi-mission ICME catalogs.

The paper concludes with a caveat that the absence of an independent "ground truth" means methodological choices (signatures, thresholds) will always drive systematic differences between catalogs. However, this work establishes a robust baseline for future comparative studies and space weather analysis.