The FPGA based Continuous FFT Tune Measurement System for the LHC and its Test at the CERN SPS
Bibliographic record
Abstract
A base band tune (BBQ) measurement system has recently been developed at CERN based on a high-sensitivity direct-diode detection technique followed by a high resolution FFT algorithm implemented in an FPGA. The FPGA based digital processing allows the acquisition of continuous real-time spectra with 32-bit resolution, while a digital frequency synthesiser (DFS) can provide acquisition synchronised chirp excitation. All the implemented algorithms support dynamic reconfiguration of processing and excitation parameters. Results from both laboratory measurements and tests performed with beam at the CERN SPS will be presented. SYSTEM OVERVIEW The betatron tune can be measured observing the small oscillation of the beam position at a fixed location. In the BBQ system developed at CERN this is done by using diodes to detect the envelope of the signal from stripline pickups [1]. This signal, filtered and amplified by an analogue front end, is digitized at a frequency multiple of the revolution frequency using a NIM module equipped with a 24bit audio codec. The codec control and all the subsequent processing is performed in the FPGA of a general purpose VME board developed at TRIUMF (Canada). The chirp excitation required to detect the betatron frequency in the spectra is provided by the FPGA via a fully programmable digital frequency synthesizer (DFS) synchronised with the acquisition start. Thanks to the high sensitivity of the direct diode technique this excitation can be kept at or below the micron level, while in some cases just the residual beam motion is sufficient to detect the tune peak in the spectra. THE DIGITAL PROCESSING CHAIN The data processing architecture implemented inside the FPGA allows real time beam spectra to be continuously calculated while also allowing on-the-fly reconfiguration of all acquisition, processing and excitation parameters.
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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) | 0.023 | 0.006 |
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".