Model of a dynamic orbit correction system based on neural network in CLS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it