| 1. |
- Akiba, K., et al.
(författare)
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Measurement of thermal properties of the LHCb VELO detector using track-based software alignment
- 2023
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Ingår i: Journal of Instrumentation. - : Institute of Physics (IOP). - 1748-0221. ; 18:10
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Tidskriftsartikel (refereegranskat)abstract
- The thermal properties of the LHCb Vertex Locator (VELO) are studied using the real-time detector alignment procedure. The variation of the position and orientation of the detector elements as a function of the operating temperature of the VELO is presented. This study uses a dataset collected by the LHCb experiment during a VELO temperature scan performed at the end of LHC Run 2 (October 2018). Significant shrinkage of the VELO modules is observed at the operating temperature of -30(degrees)C compared to the laboratory measurements on a single module taken at a range of temperatures from +45(degrees)C to -25(degrees)C. The thermal shrinkage expected from the extrapolation of laboratory measurements to lower temperatures, and the results of this alignment study are in good agreement.
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| 2. |
- Alimena, Juliette, et al.
(författare)
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Searching for long-lived particles beyond the Standard Model at the Large Hadron Collider
- 2020
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Ingår i: Journal of Physics G. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 47:9
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Tidskriftsartikel (refereegranskat)abstract
- Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton-proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments-as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER-to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the high-luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity 'dark showers', highlighting opportunities for expanding the LHC reach for these signals.
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| 3. |
- Bulten, W, et al.
(författare)
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Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
- 2022
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Ingår i: Nature medicine. - : Springer Science and Business Media LLC. - 1546-170X .- 1078-8956. ; 28:21, s. 154-
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.
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