DELILA: A Scalable Data Acquisition System for Multi-Detector Nuclear Physics Experiments at ELI-NP
Bibliographic record
Abstract
Modern nuclear physics experiments require flexible data acquisition (DAQ) systems for high data rates from diverse detector arrays. This paper presents DELILA (Digital ELI-NP List-mode Acquisition), an open-source DAQ system for high-throughput experiments at the Extreme Light Infrastructure – Nuclear Physics (ELI-NP) facility. DELILA employs a distributed, modular architecture with five components: Data Sources, Merger, Data Sinks, User Interface (UI), and Application Programming Interface (API). The system supports triggerless list-mode acquisition from silicon strips, scintillators, and germanium detectors using CAEN digitizers with various firmware (FW) (Digital Pulse Processing - Pulse Shape Discrimination (DPP-PSD), Pulse Height Discrimination (DPP-PHA), Charge Integration (DPP-QDC), waveform recording). Key features include robust clock synchronization, network transparency for distributed control, and optimized low-latency data transfer. Performance evaluation demonstrates sustained acquisition at 100 kHz per channel with FELib libraries and 2 MHz for single-channel 2730 digitizers. The system achieves 60 MB/s data rates with compact 30-byte events, enabling efficient storage. DELILA scales from 3 to 320 channels with real-time monitoring and event rate displays updated every 1-10 seconds. The Angular-based web interface with RESTful API facilitates remote operation and automated monitoring integration. Comprehensive testing using PuppEx signal generation validated stable operation across multiple digitizer configurations, with performance limited by disk I/O rather than acquisition electronics. DELILA’s open-source design promotes collaborative development and customization for diverse nuclear physics experimental requirements [1].
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".