Development, Validation and Integration of the ATLAS Trigger System Software in Run 2
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The trigger system of the ATLAS detector at the LHC is a combination of hardware, firmware and software, associated to various sub-detectors that must seamlessly cooperate in order to select 1 collision of interest out of every 40,000 delivered by the LHC every millisecond. This talk will discuss the challenges, workflow and organization of the ongoing trigger software development, validation and deployment. This development, from the top level integration and configuration to the individual components responsible for each sub system, is done to ensure that the most up to date algorithms are used to optimize the performance of the experiment. This optimization hinges on the reliability and predictability of the software performance, which is why validation is of the utmost importance. The software adheres to a hierarchical release structure, with newly validated releases propagating upwards. Integration tests are carried out on a daily basis to ensure that the releases deployed to the online trigger farm during data taking run as desired. Releases at all levels are validated by fully reconstructing the data from the raw files of a benchmark run, mimicking the reconstruction that occurs during normal data taking. This exercise is computationally demanding and thus runs on the ATLAS high performance computing grid with high priority. Performance metrics ranging from low level memory and CPU requirements, to shapes and efficiencies of high level physics quantities are visualized and validated by a range of experts. This is a multifaceted critical task that ties together many aspects of the experimental effort that directly influences the overall performance of the ATLAS experiment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it