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Record W6989457721

ATLAS Search For Resonances Decaying Into Boosted Top Quark Pairs

2019· other· en· W6989457721 on OpenAlexaff

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAtlas (anatomy)HadronTop quarkLarge Hadron ColliderInvariant (physics)ATLAS experimentInvariant mass
DOInot available

Abstract

fetched live from OpenAlex

Many Beyond Standard Model theories predict an increased number of boosted top quark events making the ttbar system an important stepping stone in the search for new physics. The ATLAS experiment has just finished its second run December 2018 with a total of 140 fb-1 worth of data collected through 2015-2018, with this luminosity, the run 2 data allows an analysis of a greater number of boosted Top quark events with P_T > 500 GeV. This analysis focuses on highly boosted ttbar events decaying via an all hadronic channel being compared with predicted values near high ttbar invariant mass. The prediction values are calculated via Monte Carlo samples for the ttbar decays and a data driven estimate for multijet background. This poster summarizes a simple counting experiment done using a BumpHunter algorithm in a search for a general excess in boosted ttbar events.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.004

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.

Opus teacher head0.032
GPT teacher head0.296
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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Same venueCERN Document Server (European Organization for Nuclear Research)French-language works237,207