Searches for beyond the Standard Model physics with boosted topologies in the ATLAS experiment using the Grid-based Tier-3 facility at IFIC-Valencia
Bibliographic record
Abstract
Both the LHC and ATLAS have been performing well beyond expectation since the start of the data taking by the end of 2009. Since then, several thousands of millions of collision events have been recorded by the ATLAS experiment. With a data taking efficiency higher than 95% and more than 99% of its channels working, ATLAS supplies data with an unmatched quality. In order to analyse the data, the ATLAS Collaboration has designed a distributed computing model based on GRID technologies. The ATLAS computing model and its evolution since the start of the LHC is discussed in section 3.1. The ATLAS computing model groups the different types of computing centers of the ATLAS Collaboration in a tiered hierarchy that ranges from Tier-0 at CERN, down to the 11 Tier-1 centers and the nearly 80 Tier-2 centres distributed world wide. The Spanish Tier-2 activities during the first years of data taking are described in section 3.2. Tier-3 are institution-level non-ATLAS funded or controlled centres that participate presumably most frequently in support of the particular interests of local physicists. Sections 3.3 and 3.4 cover IFIC-Valencia Tier-3 prototype, its design, deployment and performance. As the LHC explores a new energy regime, heavy Standard Model particles like the W and Z gauge bosons and the top quark are frequently produced with a momentum that considerably exceeds their mass. Only a handful of $t\bar{t}$ pairs with a mass over 1 TeV were created at the Tevatron. In the $\sim 20~fb^{-1}$ of proton-proton collisions at 8 TeV of the first run of the LHC there are tens of thousands of $t\bar{t}$ pairs, and they will be counted by the millions when the LHC reaches its 14 TeV design energy after the 2013-2014 shutdown. These objects are already a crucial part of the physics programme of the LHC and their importance is bound to increase in the future. The Lorentz boost of these particles alters the observed topology in an important way. The partons from the decay of the boosted particle are collimated into a smaller area and standard jet algorithms no longer resolve the resulting partons individually. The decay products are merged within a single jet. Highly boosted objects thus represent a challenge to the conventional object identication and isolation criteria, that were developed primarily for particles approximately at rest in the laboratory frame. Rather than trying to resolve the jets individually, the complete decay is reconstructed as a single fat jet. The composite nature of the jet is revealed by an analysis of the substructure of the jet. The first measurement of the invariant mass and $k_t$ splitting scales of fat anti-$k_t$ jets and the use of these variables to identify the first boosted top quarks ever seen is shown in chapter 4. Several models of new physics beyond the Standard Model predict new resonances strongly coupled to the top quark. A well known example is the leptophobic resonance in topcolor models. More recent proposals predict a heavy partner of the gluon in certain models with additional spatial dimensions. These Kaluza Klein gluons do not couple to leptons, and the quarks of the third family are favourite compared to lighter quarks. $t\bar{t}$ resonances are the main signal used in searches for these models. Therefore, the study of boosted top quark topologies and jet substructure plays a crucial role as a tool for discovery. Chapter 5 constitutes the first application of the boosted paradigm in ATLAS data. This effort has been able to push the limits on the benchmark models used further up proving that reconstruction techniques aimed at boosted objects can greatly enhance the potential of searches for new physics beyond the SM in the LHC.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".