Underlying-event studies with strange hadrons in pp collisions at s=13 TeV with the ATLAS detector.

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Title: Underlying-event studies with strange hadrons in pp collisions at s=13 TeV with the ATLAS detector.
Authors: ATLAS Collaboration (AUTHOR), Aad, G.1 (AUTHOR), Aakvaag, E.2 (AUTHOR), Abbott, B.3 (AUTHOR), Abdelhameed, S.4 (AUTHOR), Abeling, K.5 (AUTHOR), Abicht, N. J.6 (AUTHOR), Abidi, S. H.7 (AUTHOR), Aboelela, M.8 (AUTHOR), Aboulhorma, A.9 (AUTHOR), Abramowicz, H.10 (AUTHOR), Abreu, H.11 (AUTHOR), Abulaiti, Y.12 (AUTHOR), Acharya, B. S.13,14,15 (AUTHOR), Ackermann, A.16 (AUTHOR), Bourdarios, C. Adam17 (AUTHOR), Adamczyk, L.18 (AUTHOR), Addepalli, S. V.19 (AUTHOR), Addison, M. J.20 (AUTHOR), Adelman, J.21 (AUTHOR)
Source: European Physical Journal C -- Particles & Fields. Dec2024, Vol. 84 Issue 12, p1-29. 29p.
Subjects: Monte Carlo method, Hadron interactions, Hadrons, Detectors, Number theory
Abstract: Properties of the underlying-event in pp interactions are investigated primarily via the strange hadrons K S 0 , Λ and Λ ¯ , as reconstructed using the ATLAS detector at the LHC in minimum-bias pp collision data at s = 13 TeV. The hadrons are reconstructed via the identification of the displaced two-particle vertices corresponding to the decay modes , Λ → π - p and Λ ¯ → π + p ¯ . These are used in the construction of underlying-event observables in azimuthal regions computed relative to the leading charged-particle jet in the event. None of the hadronisation and underlying-event physics models considered can describe the data over the full kinematic range considered. Events with a leading charged-particle jet in the range of 10 < p T ≤ 40 GeV are studied using the number of prompt charged particles in the transverse region. The ratio N (Λ + Λ ¯) / N (K S 0) as a function of the number of such charged particles varies only slightly over this range. This disagrees with the expectations of some of the considered Monte Carlo models. [ABSTRACT FROM AUTHOR]
Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Underlying-event studies with strange hadrons in pp collisions at s=13 TeV with the ATLAS detector.
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  Data: Properties of the underlying-event in pp interactions are investigated primarily via the strange hadrons K S 0 , Λ and Λ &#175; , as reconstructed using the ATLAS detector at the LHC in minimum-bias pp collision data at s = 13 TeV. The hadrons are reconstructed via the identification of the displaced two-particle vertices corresponding to the decay modes , Λ → π - p and Λ &#175; → π + p &#175; . These are used in the construction of underlying-event observables in azimuthal regions computed relative to the leading charged-particle jet in the event. None of the hadronisation and underlying-event physics models considered can describe the data over the full kinematic range considered. Events with a leading charged-particle jet in the range of 10 &lt; p T ≤ 40 GeV are studied using the number of prompt charged particles in the transverse region. The ratio N (Λ + Λ &#175;) / N (K S 0) as a function of the number of such charged particles varies only slightly over this range. This disagrees with the expectations of some of the considered Monte Carlo models. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of European Physical Journal C -- Particles &amp; Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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