The performance of missing transverse momentum reconstruction and its significance with the ATLAS detector using 140 fb-1 of s=13 TeV pp collisions
Bibliographic record
Abstract
This paper presents the reconstruction of missing transverse momentum (pTmiss$$p_{\text {T}}^{\text {miss}}$$) in proton–proton collisions, at a center-of-mass energy of 13 TeV. This is a challenging task involving many detector inputs, combining fully calibrated electrons, muons, photons, hadronically decaying τ$$\tau $$-leptons, hadronic jets, and soft activity from remaining tracks. Possible double counting of momentum is avoided by applying a signal ambiguity resolution procedure which rejects detector inputs that have already been used. Several pTmiss$$p_{\text {T}}^{\text {miss}}$$ ‘working points’ are defined with varying stringency of selections, the tightest improving the resolution at high pile-up by up to 39% compared to the loosest. The pTmiss$$p_{\text {T}}^{\text {miss}}$$ performance is evaluated using data and Monte Carlo simulation, with an emphasis on understanding the impact of pile-up, primarily using events consistent with leptonic Z decays. The studies use 140fb-1$$140~\text {fb}^{-1}$$ of data, collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. The results demonstrate that pTmiss$$p_{\text {T}}^{\text {miss}}$$ reconstruction, and its associated significance, are well understood and reliably modelled by simulation. Finally, the systematic uncertainties on the soft pTmiss$$p_{\text {T}}^{\text {miss}}$$ component are calculated. After various improvements the scale and resolution uncertainties are reduced by up to 76%$$76\%$$ and 51%$$51\%$$, respectively, compared to the previous calculation at a lower luminosity.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".