Development of a Very Faint Meteor Detection System based on an EMCCD Sensor and Matched Filter Processing
Bibliographic record
Abstract
The mass ranges of meteors, imaged by electro-optical (EO) cameras and backscatter radar receivers, for the most part do not overlap. Typical EO systems detect meteoroid masses down to 10$^{-5}$ kg or roughly magnitude +2 meteors when using moderate field of view optics, un-intensified optical components, and meteor entry velocities around 45 km/sec. This is near the high end of the mass range of typical meteor radar observations. Having the same mass meteor measured by different sensor wavelength bands would be a benefit in terms of calibrating mass estimations for both EO and radar. To that end, the University of Western Ontario (UWO) has acquired and deployed a very low light imaging system based on an electron-multiplying CCD camera technology. This embeds a very low noise, per pixel intensifier chip in a cooled camera setup with various options for frame rate, region of interest and binning. The EO system of optics and sensor was optimally configured to collect 32 frames per second in a square field of view 14.7 degrees on a side, achieving a single-frame stellar limiting magnitude of m$_G$ = +10.5. The system typically observes meteors of +6.5. A key development in this pipeline has been the first true application of matched filter processing to process the faintest meteors possible in the EMCCD system while also yielding high quality automated metric measurements of meteor focal plane positions. With pairs of EMCCD systems deployed at two sites, triangulation and high accuracy orbits are one of the many products being generated by this system. These measurements will be coupled to observations from the Canadian Meteor Orbit Radar (CMOR) used for meteor plasma characterization and the Canadian Automated Meteor Observatory (CAMO) high resolution mirror tracking system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.003 |
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".