Pattern Recognition Using SVM for the Classification of the Size and Distance of Trans-Neptunian Objects Detected by Serendipitous Stellar Occultations
Bibliographic record
Abstract
Abstract We present a new pipeline based on the Support Vector Machine algorithm to confirm the detection and perform classification of small solar system objects by serendipitous stellar occultations. This pipeline is designed to analyze light curves and to identify the occultation events and the classification of the occulting bodies according to their size, typically from a fraction to a few kilometers, and their distance from the Sun, typically a few tens of astronomical units. The input light curves for this pipeline were obtained from the event simulator for the Trans-Neptunian Automated Occultation Survey (TAOS II). We explore parameters affecting occultation light curves such as spectral type, apparent magnitude and finite angular size of the occulted star, angle from opposition, and readout cadence for the observations; also we assumed a Poisson noise distribution as expected from the TAOS II project. We find that occultation events, especially by trans-Neptunian objects with diameters ≥2 km are detected with 99.99%, 99.53%, and 86% efficiency for stars with a visual apparent magnitude of 12, 14, and 16, respectively at 0.05 s of exposure time. In terms of size and distance classification the overall accuracy is 94%. However, for smaller objects the confirmation and classification depends mostly upon the signal-to-noise ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".