| 1. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 2. |
- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t
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| 3. |
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| 4. |
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| 5. |
- Aad, G., et al.
(författare)
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The ATLAS Simulation Infrastructure
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 823-874
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Tidskriftsartikel (refereegranskat)abstract
- The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.
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| 6. |
- Aad, G., et al.
(författare)
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Commissioning of the ATLAS Muon Spectrometer with cosmic rays
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:3, s. 875-916
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Tidskriftsartikel (refereegranskat)abstract
- The ATLAS detector at the Large Hadron Collider has collected several hundred million cosmic ray events during 2008 and 2009. These data were used to commission the Muon Spectrometer and to study the performance of the trigger and tracking chambers, their alignment, the detector control system, the data acquisition and the analysis programs. We present the performance in the relevant parameters that determine the quality of the muon measurement. We discuss the single element efficiency, resolution and noise rates, the calibration method of the detector response and of the alignment system, the track reconstruction efficiency and the momentum measurement. The results show that the detector is close to the design performance and that the Muon Spectrometer is ready to detect muons produced in high energy proton-proton collisions.
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| 7. |
- Shraim, M. A., et al.
(författare)
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Features and methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system: a Delphi expert consensus study
- 2022
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Ingår i: Pain. - : Ovid Technologies (Wolters Kluwer Health). - 0304-3959 .- 1872-6623. ; 163:9, s. 1812-1828
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Tidskriftsartikel (refereegranskat)abstract
- Classification of musculoskeletal pain based on underlying pain mechanisms (nociceptive, neuropathic, and nociplastic pain) is challenging. In the absence of a gold standard, verification of features that could aid in discrimination between these mechanisms in clinical practice and research depends on expert consensus. This Delphi expert consensus study aimed to: (1) identify features and assessment findings that are unique to a pain mechanism category or shared between no more than 2 categories and (2) develop a ranked list of candidate features that could potentially discriminate between pain mechanisms. A group of international experts were recruited based on their expertise in the field of pain. The Delphi process involved 2 rounds: round 1 assessed expert opinion on features that are unique to a pain mechanism category or shared between 2 (based on a 40% agreement threshold); and round 2 reviewed features that failed to reach consensus, evaluated additional features, and considered wording changes. Forty-nine international experts representing a wide range of disciplines participated. Consensus was reached for 196 of 292 features presented to the panel (clinical examination-134 features, quantitative sensory testing-34, imaging and diagnostic testing-14, and pain-type questionnaires-14). From the 196 features, consensus was reached for 76 features as unique to nociceptive (17), neuropathic (37), or nociplastic (22) pain mechanisms and 120 features as shared between pairs of pain mechanism categories (78 for neuropathic and nociplastic pain). This consensus study generated a list of potential candidate features that are likely to aid in discrimination between types of musculoskeletal pain.
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