Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC
Bibliographic record
Abstract
A new era of hadron collisions will start around 2029 with the High-Luminosity LHC which is designed to collect ten times more data than what has been collected during 10 years of operation at LHC. This will be achieved by higher instantaneous luminosity at the price of a higher number of collisions per bunch crossing. In order to withstand the high expected radiation doses and the harsher data taking conditions, the ATLAS Liquid Argon Calorimeter readout electronics is being upgraded. The electronic readout chain is composed of four main components. 1) New front-end boards will amplify, shape, and digitise the calorimeter’s ionisation signal using two gains over a dynamic range of 16 bits and 11 bit precision. 2) New calibration boards will provide precise calibration of all 182468 channels of the calorimeter over a 16 bit dynamic range. 3) New ATCA compliant signal processing boards (“LASP”) will receive the detector data at 40 MHz, where FPGAs connected through lpGBT high-speed links will perform energy and time reconstruction. In total, the off-detector electronics receive 345 Tbps of data via 33000 links at 10 Gbps. For the first time, online machine learning techniques are being considered for use in these FPGAs. A subset of the original data is sent with low latency to the hardware trigger system, while the full data are buffered until receiving of trigger accept signals. The latest status of the development of the board and the firmware is shown. 4) A new timing and control system, “LATS”, will synchronise the aforementioned components. Its current design status will also be shown.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".