Model of a dynamic orbit correction system based on neural network in CLS
Bibliographic record
Abstract
In CLS, Deep Learning was applied to make a dynamic model for the Orbit Correction System (OCS). The OCS consists of 48 sets of BPMs BERGOZ (96 data sheets with 900 Hz recording) that measure the beam position and use the SVD matrix to calculate the strength of the orbit correctors (48 sets of Orbit Correctors 'OC'). The Neural Network was built, trained, and tested using 96 BPM signals. Five layers of the network (Input Layer, Three Hidden Layers, and Output Layer) provide the time evolution of OC's signals (18 Hz), which can be achieved with high accuracy (Mean Square Error = 10e-7). The results are based on data collected during all challenging situations of the CLS storage ring’s current beam position. An Arduino Board was used to test this methodology in real-time, and the time of operation was within the range of system timing (30 - 40 microseconds).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".