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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2023). First observation of the B+ → Ds+ Ds- K+ decay. Phys. Rev. D, 108(3), 034012–14pp.
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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2023). Search for KS(L)0 → μ+ μ- μ+ μ- decays at LHCb. Phys. Rev. D, 108(3), L031102–12pp.
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Krupczak, R., da Silva, T. N., Domingues, T. S., Luzum, M., Denicol, G. S., Gardim, F. G., et al. (2024). Causality violations in simulations of large and small heavy-ion collisions. Phys. Rev. C, 109(3), 034908–12pp.
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LHCb Collaboration(Aaij, R. et al), Jaimes Elles, S. J., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Rebollo De Miguel, M., et al. (2024). Measurement of Ξc+ production in pPb collisions at √sNN=8.16 TeV at LHCb. Phys. Rev. C, 109(4), 044901–14pp.
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IDS Collaboration(Andel, B. et al), Algora, A., & Nacher, E. (2024). β decay of the ground state and of a low-lying isomer in Bi-216. Phys. Rev. C, 109(6), 064321–18pp.
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Nascimento, J. R., Olmo, G. J., Petrov, A. Y., & Porfirio, P. J. (2024). On metric-affine bumblebee model coupled to scalar matter. Nucl. Phys. B, 1004, 116577–10pp.
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Miyagawa, P. S. et al, Bernabeu, P., Lacasta, C., Solaz, C., & Soldevila, U. (2024). Analysis of the results from Quality Control tests performed on ATLAS18 Strip Sensors during on-going production. Nucl. Instrum. Methods Phys. Res. A, 1064, 169457–9pp.
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Bach, E. et al, Bernabeu, J., Lacasta, C., Solaz, C., & Soldevila, U. (2024). Analysis of the quality assurance results from the initial part of production of the ATLAS18 ITK strip sensors. Nucl. Instrum. Methods Phys. Res. A, 1064, 169435–8pp.
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Martins, A., da Mota, A. F., Stanford, C., Contreras, T., Martin-Albo, J., Kish, A., et al. (2024). Simple strategy for the simulation of axially symmetric large-area metasurfaces. J. Opt. Soc. Am. B, 41(5), 1261–1269.
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CALICE Collaboration(Lai, S. et al), & Irles, A. (2024). Software compensation for highly granular calorimeters using machine learning. J. Instrum., 19(4), P04037–28pp.
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