Speaker
Description
We present the first large-scale statistical analysis of an extensive catalogue of interplanetary Type III solar radio bursts compiled through human participation. The catalogue is based on Radio and Plasma Waves (RPW) observations from Solar Orbiter, collected through the citizen-science campaign Solar Radio Burst Tracker hosted on Zooniverse, with contributions from 867 volunteers between February 2020 and March 2025.
The final catalogue contains 15,934 Type III bursts, which were systematically compared with solar activity phenomena. We find clear correlations with solar flares, while short-scale deviations indicate that Type III bursts are not solely determined by flare energetics but are also strongly influenced by coronal and interplanetary plasma conditions governing electron-beam propagation and wave–particle interactions. Furthermore, burst characteristics show a measurable dependence on flare energy class.
Analysis of peak flux across 23 frequencies reveals power-law behavior with an average slope of −1.57 ± 0.01, steepening toward higher frequencies. At still higher frequencies, the slopes plateau around −1.7 to −1.8, resembling scaling behavior consistent with self-organized criticality. Burst occurrence peaks at 1–2 MHz, suggesting particularly efficient electron-beam transport and beam–plasma interaction in this frequency range.
Frequency drift rates derived for 13,074 bursts follow the empirical relation
df/dt = −0.002 f¹·³⁷, differing from previously reported relations over other frequency ranges. Notably, the drift-rate trend shows an enhancement near 1–2 MHz, coincident with the occurrence maximum, likely reflecting the influence of plasma density structure in the inner heliosphere.
This human-validated, large-scale catalogue provides a robust statistical resource for Type III burst studies, capturing solar-cycle and observational effects, extending analyses to faint and complex events, and placing new constraints on frequency-dependent flux and drift behavior. The catalogue further establishes a benchmark dataset for future automated and machine-learning detection methodologies.
| Talk category | NOVA Network 2 |
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