CME Source Region Catalogue

• The study constructs a comprehensive CME source region catalogue from 3327 events spanning solar cycles 23 and 24, linking low-coronal origins to observed kinematics.
• It categorizes source regions into active regions, prominence eruptions, and active prominences, revealing distinct patterns in occurrence, speed distributions, and deflection behaviors.
• The analysis demonstrates that source-region characteristics imprint on CME dynamics, influencing power-law speed behavior, latitudinal deflection, and East–West asymmetry.

Searching arXiv for related CME source-region and deflection papers to ground contextual statements. arxiv_search(query="coronal mass ejection source region prominence eruption active region deflection solar cycle", max_results=10, sort_by="relevance") A coronal mass ejection source region catalogue is a structured association between observed coronal mass ejections (CMEs) and the low-coronal solar regions from which they originate. The reported study assembles such a catalogue for 3327 CMEs from 1998 to 2017, spanning different phases of solar cycles 23 and 24, and uses it to examine how source-region type is reflected in CME occurrence, projected speed, latitudinal deflection, and longitudinal distribution. In the study, source regions are separated into active regions (ARs), prominence eruptions (PEs), and active prominences (APs), while CMEs are divided into slow and fast according to average projected speed. The central conclusion is that multiple aspects of CME kinematics bear a strong imprint of source regions, implying different ejection and/or propagation mechanisms (Majumdar et al., 2023).

1. Catalogue scope and scientific objective

The primary objective of the study is to connect CMEs to their source regions and thereby construct a CME source region catalogue. A second objective is to probe the influence of those source regions on the statistical properties of CMEs. The catalogue contains 3327 CMEs covering 1998–2017, a span chosen to capture different phases of cycles 23 and 24 (Majumdar et al., 2023).

Within that framework, the source-region problem is not merely classificatory. It links a coronagraphic manifestation—the outward-expanding CME seen in projection—to an origin in the low corona, where different magnetic environments can plausibly modulate acceleration, geometry, and propagation. The study therefore treats source attribution as a way to test whether CME populations are homogeneous or whether distinct solar environments leave distinct statistical signatures.

A plausible implication is that a source-region catalogue of this kind functions as a bridge between event morphology and solar-cycle evolution. By sampling two full solar cycles in part, it allows slow/fast occurrence, speed distributions, and directional asymmetries to be compared not only across eruption classes but also across rising, maximum, declining, and minimum phases, even though the exact phase-resolved counts and fitted parameters are not given in the available description.

2. Source-region taxonomy and event association

The reported catalogue segregates source regions into three classes: ARs, PEs, and APs (Majumdar et al., 2023). The accompanying methodological description presents these classes in standard operational terms.

Source class	Typical definition	Typical low-coronal signature
AR	Eruptions rooted within NOAA-numbered magnetically complex regions	Flares, hot EUV loops, strong-field PILs
PE	Quiet-Sun filament or prominence eruptions away from ARs	Cool material lifting in H-alpha/EUV 304 Å
AP	Filament or prominence eruptions embedded within or adjacent to ARs	Filament lift-off plus AR flare/loop response

In that standard taxonomy, AR CMEs are associated with compact, strong-field environments; PEs with large-scale, weaker-field filament systems outside active regions; and APs with hybrid settings in which filament mass and active-region field coexist. This classification matters because it distinguishes eruptions dominated by impulsive AR reconnection from those dominated by filament destabilization or mixed processes.

The same methodological summary describes a typical event-association workflow. Coronagraph observations from SOHO/LASCO C2 and C3 are temporally matched to low-coronal signatures seen in EUV imagers such as SOHO/EIT or SDO/AIA, to H-alpha prominence observations, and to contextual magnetograms from SOHO/MDI or SDO/HMI. A typical temporal window is $\Delta t \approx \pm 1$–$2$ hr, with spatial consistency imposed between the CME central position angle and the candidate source latitude and longitude. Where multiple candidate sources exist, temporal coincidence, eruptive morphology, and position-angle consistency are typically prioritized.

This suggests that the catalogue’s explanatory power depends not only on the number of CMEs but also on the stability of this association procedure across instrumental eras. The description explicitly notes the transition from EIT to AIA and from MDI to HMI as a potential source of nonuniformity, which is significant for any interpretation of long-baseline trends.

3. Speed classes, projected kinematics, and power-law behavior

The CMEs in the catalogue are separated into slow and fast according to their average projected speeds (Majumdar et al., 2023). The methodological description defines average projected speed in the usual way from LASCO height–time measurements $R(t)$ of the leading edge, either as

$$v_{\mathrm{avg}} = \frac{R_{\mathrm{last}} - R_{\mathrm{first}}}{t_{\mathrm{last}} - t_{\mathrm{first}}}$$

or as the slope of a linear regression through all reliable height–time points.

Because these are plane-of-sky measurements, they are subject to projection bias. The description therefore distinguishes projected speed from true radial speed and notes that deprojection is nontrivial without three-dimensional geometry. That caveat is essential: the reported source-region dependence concerns projected kinematics, not a uniquely reconstructed three-dimensional flow field.

The study finds that the contribution of the three source-region types to the occurrences of slow and fast CMEs is different over the interval considered (Majumdar et al., 2023). It further finds that distributions of average speed follow different power laws depending on source type, and that the relevant power law changes across different phases of cycles 23 and 24 (Majumdar et al., 2023). The summary does not provide the fitted exponents, fit ranges, or uncertainties, but it does specify the standard functional forms typically used in such analyses:

$P(v) = A v^{-\alpha}$

for differential speed distributions, and

$N(>v) = C v^{-\beta}$

for cumulative distributions, with $\beta = \alpha - 1$ in the pure power-law case.

A plausible implication is that source-region class is not merely correlated with mean speed but with the full tail structure of the speed distribution. In that reading, AR-, PE-, and AP-origin CMEs would not be distinguished only by central tendency; they would populate the high-speed end differently, and that distinction would itself vary with solar-cycle phase.

4. Latitudinal deflection and East–West asymmetry

The study reports equator-ward latitudinal deflections in a statistical analysis of CME trajectories, and it states that the magnitude of these deflections bears an imprint of the source regions (Majumdar et al., 2023). The methodological description formalizes this with a source latitude $\theta_{\mathrm{src}}$, a CME latitude $\theta_{\mathrm{CME}}$ derived from coronagraph position angle, and a deflection angle

$$\Delta \theta = \theta_{\mathrm{CME}} - \theta_{\mathrm{src}}.$$

In that framework, source-region dependence enters through the distribution of $2$0 or $2$1 across AR, PE, and AP events. Although the available text does not supply means, medians, or confidence intervals, it explicitly frames the problem in terms of comparing statistical deflection signatures among source classes and across cycle phases.

The same study also identifies an East–West asymmetry, especially during the rising phase of cycle 23, and notes the presence of active longitudes for CMEs, with a preference toward the western part of the Sun (Majumdar et al., 2023). The associated standard summary introduces the usual asymmetry index,

$2$2

where $2$3 and $2$4 denote western and eastern counts.

This is important because it separates two directional effects. Latitudinal deflection concerns propagation relative to the solar equator, whereas East–West asymmetry concerns the longitudinal distribution of source occurrence or detectability. The western preference noted for the rising phase of cycle 23 therefore points to organized longitudinal structure—described as active longitudes—rather than to a purely random azimuthal spread.

5. Physical interpretation of source-region imprint

The central interpretive statement of the study is that different aspects of CME kinematics bear a strong imprint of the source regions they originate from, indicating the existence of different ejection and/or propagation mechanisms (Majumdar et al., 2023). That claim can be unpacked into two linked propositions.

First, the source region likely influences the launch conditions. In the standard physical reading summarized alongside the paper, AR CMEs are associated with strong, sheared, compact magnetic fields and high reconnection rates, conditions that are consistent with more impulsive acceleration. PEs are associated with large-scale, weak-field filament systems whose gradual rise and loss of equilibrium are consistent with slower CMEs. APs occupy an intermediate regime in which filament mass is embedded within active-region field and hybrid behavior is expected.

Second, the source region may influence propagation after launch. The same summary suggests that CMEs from weaker-field, filament-dominated environments may be more susceptible to lateral guidance by the ambient corona, including coronal-hole pressure gradients and the global streamer belt, whereas more impulsive AR CMEs may show reduced deflection because of stronger internal magnetic pressure or larger inertia. This suggests that a source-region imprint can survive beyond initiation and remain visible in projected kinematics.

A common misconception is to treat CME source-region class as merely descriptive morphology. The study’s framing rejects that reduction: source class is presented as a physically relevant organizing variable that correlates with occurrence statistics, speed-law behavior, deflection, and longitudinal asymmetry rather than with appearance alone (Majumdar et al., 2023).

6. Methodological boundaries, uncertainties, and evidentiary status

The available description is explicit that it does not provide the catalogue-level numbers, fitted relations, slopes, or uncertainties beyond the 3327-event total and the qualitative findings stated in the abstract. It therefore does not supply the exact slow/fast threshold, phase-by-phase event counts, power-law indices, deflection moments, or significance tests. Any stronger quantitative reconstruction would exceed the evidence presently given.

That limitation is consequential for interpretation. For example, the framework describes typical cycle-phase boundaries, typical slow/fast thresholds such as 400–500 km s$2$5 for slow and $2$6–$2$7 km s$2$8 for fast, and typical instruments and association rules, but it does not state that these exact operational choices were used in the reported catalogue. Likewise, it gives typical power-law forms and asymmetry metrics, yet not the fitted parameters that would be needed for a numerical meta-analysis.

The summary also identifies several sources of bias that are structurally important in this field: projection effects in plane-of-sky speeds; incompleteness due to selection thresholds and data gaps; changing instrumental sensitivity across the EIT/AIA and MDI/HMI transitions; and ambiguities in associating coronagraphic events with low-coronal eruptions. These considerations imply that the catalogue is best understood as a statistically organized mapping between CMEs and source classes, not as an uncertainty-free reconstruction of CME origin physics.

Even with those constraints, the study’s evidentiary core is clear. It establishes a source-region catalogue spanning 1998–2017, classifies sources into AR, PE, and AP, separates CMEs into slow and fast by projected speed, and reports systematic source-dependent differences in occurrence, speed-law behavior, latitudinal deflection, and East–West asymmetry across solar cycles 23 and 24 (Majumdar et al., 2023). That combination makes source-region classification central to the statistical study of CME kinematics rather than peripheral to it.