Dijet production in $\sqrt{s}=7$ TeV $pp$ collisions with large rapidity gaps at the ATLAS experiment
Bibliographic record
Abstract
CERN-LHC. Measurements of the pseudorapidity gap and xi distributions for dijet production in diffractive events which are defined by large gaps devoid of activity seen in the ATLAS detector, based on a sample of proton-proton collisions at a centre-of-mass energy of 7 TeV of integrated luminosity of 6.8 nb-1 collected in 2010. The final state consists of two jets found by the anti-kt jet algorithm with transverse momentum greater than 20 GeV and empty regions in the acceptance of the ATLAS detector. The pseudorapidity gap variable, denoted here as DELTA(C=RAPGAP), is defined as the larger of the pseudorapidity regions extending to the limits of the ATLAS detector sensitivity at eta=+-4.8, in which no final state particles are observed above a (transverse) momentum threshold. The xi variable is calculated using M**2/S and for Single Diffraction DELTA(C=RAPGAP)~=-ln(xi). See PLBXX (2015) YY for more details. The fiducial region is defined as: The jet finding uses the anti-kt algorithm with radius R=0.6 or R=0.4 - all stable particles are used except for muons and neutrinos - at least 2 jets with ptJet1 > 20 GeV and ptJet2 > 20 GeV - at least 2 jets with |etaJet1| < 4.4 and |etaJet2| < 4.4 The selection of particles for the gap finding: - |etaParticle| < 4.8 - pParticleNeutral > 0.2 GeV - pParticleCharged > 0.5 GeV .or. ptParticleCharged > 0.2 GeV - The gap finding: two gaps are formed defined as the regions without particles from the edge of the calorimeter at eta=4.8 or eta=-4.8. The larger of the two is the final gap. The selection of particles for the xi calculation: - |etaParticle| < 4.8 - pParticleNeutral > 0.2 GeV - pParticleCharged > 0.5 GeV - The xi value is calculated as Sum [ptParticle*exp(+-etaParticle)/sqrt(S)] where the sum loops over the particles selected above and the exponential function takes the positive (negative) sign if the gap starts at eta = +4.8 (-4.8). In the tables the individual sources of systematic uncertainty are given in per cent and are described as follows: dJES = jet energy scale uncertainty dJER = jet energy resolution uncertainty dJAR = jet angular resolution uncertainty dJRE = jet reconstruction efficiency uncertainty dClustE = cluster energy scale uncertainty dCST = cell significance threshold uncertainty dJCE = jet cleaning efficiency uncertainty dUnf = unfolding procedure uncertainty dMod = model uncertainty dTrig = jet trigger efficiency uncertainty dtrack = tracking efficiency uncertainty dlum = luminosity uncertainty dmat = added material uncertainty.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".