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Abstract A search for pair-produced vector-like quarks using events with exactly one lepton (eor$$\mu $$ ), at least four jets including at least oneb-tagged jet, and large missing transverse momentum is presented. Data from proton–proton collisions at a centre-of-mass energy of$$\sqrt{s}=$$ 13 $$\text {TeV}$$ , recorded by the ATLAS detector at the LHC from 2015 to 2018 and corresponding to an integrated luminosity of 139 fb$$^{-1}$$ , are analysed. Vector-like partnersTandBof the top and bottom quarks are considered, as is a vector-likeXwith charge$$+5/3$$ , assuming their decay into aW,Z, or Higgs boson and a third-generation quark. No significant deviations from the Standard Model expectation are observed. Upper limits on the production cross-section ofTandBquark pairs as a function of their mass are derived for various decay branching ratio scenarios. The strongest lower limits on the masses are 1.59 $$\text {TeV}$$ assuming mass-degenerate vector-like quarks and branching ratios corresponding to the weak-isospin doublet model, and 1.47 $$\text {TeV}$$ (1.46 $$\text {TeV}$$ ) for exclusive$$T \rightarrow Zt$$ ($$B/X \rightarrow Wt$$ ) decays. In addition, lower limits on theTandBquark masses are derived for all possible branching ratios.
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Machine learning classification of sphalerons and black holes at the LHC
Abstract In models with large extra dimensions, “miniature” black holes (BHs) might be produced in high-energy proton–proton collisions at the Large Hadron Collider (LHC). In the semi-classical regime, those BHs thermally decay, giving rise to large-multiplicity final states with jets and leptons. On the other hand, similar final states are also expected in the production of electroweak sphaleron/instanton-induced processes. We investigate whether one can discriminate these scenarios when BH or sphaleron-like events are observed in the LHC using machine learning (ML) methods. Classification among several BH scenarios with different numbers of extra dimensions and the minimal BH masses is also examined. In this study we consider three ML models: XGBoost algorithms with (1) high- and (2) low-level inputs, and (3) a Residual Convolutional Neural Network. In the latter case, the low-level detector information is converted into an input format of three-layer binned event images, where the value of each bin corresponds to the energy deposited in various detector subsystems. We demonstrate that only a small number of detected events are sufficient to effectively discriminate between the sphaleron and BH processes. Separation between BH scenarios with different minimal masses is possible with an order of 10 events passing the preselection. A sufficient number of events could be observed in combined Run-2 and -3 data, if the production cross section is not much smaller than the present limit$$\sim 0.1$$ fb. We find, however, that a large number of events is needed to discriminate between BH hypotheses with the same minimal BH mass, but different numbers of extra dimensions.
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- Award ID(s):
- 2210161
- PAR ID:
- 10503584
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- The European Physical Journal C
- Volume:
- 84
- Issue:
- 4
- ISSN:
- 1434-6052
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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