Bibliographic record
Abstract
The current LHC experimental program is scheduled to last until the end of 2023 and to result in a total data sample of about 300 fb − 1 at a pp centre-of-mass energy close to the design value of 14 TeV. This will be followed by a high-luminosity phase in which the LHC instantaneous luminosity will be increased by a factor of up to 7.5 times the design value of 1 × 10 34 cm − 2 s − 1 , with the goal of accumulating a total dataset of about 3000 fb − 1 over about a ten-year run period. This will require significant upgrades to both the accelerator infrastructure, and the detectors, which were not designed to operate under these conditions. The large anticipated data sample will allow for more precise investigations of topics already studied with the 300 fb − 1 data sample, as well as for studies of processes that are accessible only with the much larger statistics. There will be a particular focus on investigations of the properties of the Higgs boson, which was discovered in 2012. Rates and signal strengths will be measured for a variety of production and decay modes, allowing extraction of the Higgs boson couplings. Particular final states will allow differential cross-sections to be measured for all production modes, and for studies of the Higgs width and CP properties, as well as the tensor structure of its coupling to bosons. An important part of the HL- LHC experimental program will be investigations of the Higgs self-coupling, which is accessible via studies of di-Higgs production. The program of other Standard Model measurements will also continue at the HL-LHC. Topics that have been investigated so far, for the HL-LHC, include Vector Boson Scattering as well as topics in b - and top-quark physics. Here, projections for performance at the HL-LHC will be discussed for both ATLAS and CMS.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.029 |
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".