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- Barucca, G., et al.
(författare)
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The potential of Λ and Ξ- studies with PANDA at FAIR
- 2021
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Ingår i: European Physical Journal A. - : Springer Nature. - 1434-6001 .- 1434-601X. ; 57:4
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Tidskriftsartikel (refereegranskat)abstract
- The antiproton experiment PANDA at FAIR is designed to bring hadron physics to a new level in terms of scope, precision and accuracy. In this work, its unique capability for studies of hyperons is outlined. We discuss ground-state hyperons as diagnostic tools to study non-perturbative aspects of the strong interaction, and fundamental symmetries. New simulation studies have been carried out for two benchmark hyperon-antihyperon production channels: p¯ p→ Λ¯ Λ and p¯ p→ Ξ¯ +Ξ-. The results, presented in detail in this paper, show that hyperon-antihyperon pairs from these reactions can be exclusively reconstructed with high efficiency and very low background contamination. In addition, the polarisation and spin correlations have been studied, exploiting the weak, self-analysing decay of hyperons and antihyperons. Two independent approaches to the finite efficiency have been applied and evaluated: one standard multidimensional efficiency correction approach, and one efficiency independent approach. The applicability of the latter was thoroughly evaluated for all channels, beam momenta and observables. The standard method yields good results in all cases, and shows that spin observables can be studied with high precision and accuracy already in the first phase of data taking with PANDA.
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2. |
- Jansen, Joachim, 1989-, et al.
(författare)
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Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing
- 2023
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.
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