Pacific Ocean Neutrino Experiment first string trigger and data acquisition systems
Bibliographic record
Abstract
The Pacific Ocean Neutrino Experiment (P-ONE) is set to deploy its first detection string in the Cascadia Basin off the coast of British Columbia, Canada. As a next-generation Cherenkov neutrino telescope, P-ONE will be sensitive to ultra-high-energy neutrinos (10³–10⁸ GeV) from astrophysical sources. To effectively capture these rare physics signatures, the experiment's trigger system must operate in a high-background environment dominated by K40 decay and bioluminescence. This poster presents the design and integration of the P-ONE trigger system, which spans multiple levels of data acquisition (DAQ). The trigger must seamlessly interface with both the slow detector controlling Maximum Integrated Data Acquisition System (MIDAS) and the back-end fastDAQ system to select and preliminarily cluster events. The trigger system operates in a hierarchical fashion: first, an initial firmware (L0) trigger identifies candidate events, which are then refined by a physics trigger that requests additional waveform data from neighboring modules. Onshore, these waveform packets and timing information are assembled into full events for storage and analysis. This talk will detail the trigger chain, with a focus on bioluminescence mitigation and physics-driven event selection, as well as its integration with the DAQ and Run Control systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".