Design of a 100 Gb/s Ethernet Interface for a Silicon Photonics-based Data Acquisition System for Particle Physics Experiments
Bibliographic record
Abstract
Particle physics experiments, like nEXO and ARGO, require a large-scale data acquisition system capable of supporting high data rates. One can expect a total data rate of approximately 400 Gb/s for nEXO, whereas ARGO estimates are on a scale of Pb/s. To support those experiments, which require a low-power and high-bandwidth communication (≈1 Gb/s per link), a modular Silicon-Photonics (SiP) communication module is in development to connect the front-end electronics to the data acquisition system (DAQ). This research focuses on the interface between more than 100 optical transceivers and the DAQ. To eliminate the need for custom hardware in the servers of the DAQ, the system is compatible with a standard Ethernet network. Targetting an Ethernet interface allows the use of commodity off-the-shelf equipment to connect custom electronics to the DAQ and simplifies integration, deployment, and maintenance. This system is composed of a Zynq system-on-chip (SoC), where the FPGA receives data from a set of transceivers and wraps the frame received in UDP datagrams sent to the DAQ; the processor handles various configurations and commands. To match the requirements on data rates and modularity, this study proposes a proof of concept demonstrating a 100 Gb/s link using a FPGA to transfer the data from the custom SiP transceivers to a server running DAQ software, implemented using the MIDAS framework. Following this work, the implemented Ethernet link will constitute a system ready to integrate with the SiP communication module. This integration will provide a platform to deploy large-scale and high data-rate DAQs for the targeted experiments, namely nEXO and ARGO.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".