Low‐Cost Monitoring of Energetic Particle Precipitation: Weather Balloon‐Borne Timepix Measurements During the May 2024 Superstorm
Bibliographic record
Abstract
Abstract Understanding energetic electron precipitation is crucial for accurate space weather modeling and forecasting, impacting the Earth's upper atmosphere and human infrastructure. This study presents a low‐cost, low‐mass, and low‐power solution for high‐fidelity analysis of electron precipitation events by measuring the resulting bremsstrahlung X‐ray emissions. Specifically, we report on results from the flight of a radiation detector payload that utilized a silicon pixel read‐out “Timepix” chip, and its successful utilization onboard a “burster” weather balloon. We launched this payload during the May 2024 superstorm, capturing high‐resolution measurements of both background cosmic ray radiation as well as storm‐time energetic electron precipitation. We further developed particle and radiation detection algorithms to separate bremsstrahlung X‐rays from other particle species in the pixel‐resolved trajectories as seen in the Timepix detector. The measurements revealed a distinctive four‐peak structure in X‐ray flux, corresponding to periodic four‐minute‐long bursts of energetic electron precipitation between 21:20 and 21:40 UT. This precipitation was also observed by a riometer station close to the balloon launch path, further validating balloon measurements and the developed X‐ray identification algorithm. The clear periodic structure of the measured precipitation is likely caused by modulation of the electron losses from the radiation belt by harmonic Pc5 ultra‐low frequency waves, observed contemporaneously on the ground. The study underscores the potential of compact, low‐cost payloads for advancing our understanding of space weather. Specifically, we envision a potential use of such Timepix‐based detectors in space science, for example, on sounding rockets or nano‐, micro‐, and small satellite platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".