Event classification for a Compton/Pair Telescope and simulation studies of the COMCUBE CubeSat project for gamma-ray Astronomy
Bibliographic record
Abstract
Cosmic gamma-rays are our main observational window into the universe’s most violent processes. The observable spectrum ranges from 100 keV to several PeV. Their observation gives insight into a wide variety of astrophysical processes. Cosmic nucleosynthesis in massive stars leaves their imprint in line energies. Their measurement advances our understanding of their evolution and the distribution of heavy elements in our galaxy. Cosmic acceleration sites leave their imprint in continuous emissions, giving insight into the acceleration processes as well as the composition of the accelerated particles. Photons emitted during Gamma Ray Bursts (GRBs) are the messengers of the most extreme events in the universe. Their gamma-rays allow us to investigate the source conditions of these supernovae and merger events. The range between 0.1 to 30 MeV constitutes the ’MeV-Gap’. No mission after the Compton Gamma Ray Observatory provided improved sensitivity in this range of the spectrum. Compton/Pair telescopes can cover this range and provide additional sensitivity up to several GeV in one instrument using a segmented silicon tracking detector on top of a solid state calorimeter or liquid noble gas Time Projection Chamber. This work is divided into two main parts. The first part focuses on presenting a novel deep learning driven classification algorithm capable of providing online classification of events directly at the satellite. The task description, evaluation methods, alternative classification approaches and results are presented and discussed. The second part describes the design of the COMCUBE proposal, a CubeSat GRB polarimeter developed for the EU AHEAD 2020 initiative. CubeSats provide a potential bridge to cover the lower end of the MeV-Gap using small and cheaper mini-satellites. This work discusses performance estimates for the initial 4U detector design proposal, as well as adaptations done for the first qualification flight of the prototype during the Strato Sat 2023 balloon campaign in Timmins (Ontario). Further trigger rates for the expected radiative environment and sensitivity estimates for the Crab-Nebula are presented. The measured trigger rates are compared to simulated data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".