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Beenakker, W., Caron, S., Kip, J., Ruiz de Austri, R., & Zhang, Z. (2023). New energy spectra in neutrino and photon detectors to reveal hidden dark matter signals. J. High Energy Phys., 11(11), 028–13pp.
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Penas, J., Alejo, A., Bembibre, A., Apiñaniz, J. I., Garcia-Garcia, E., Guerrero, C., et al. (2024). Production of carbon-11 for PET preclinical imaging using a high-repetition rate laser-driven proton source. Sci Rep, 14(1), 11448–12pp.
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Contreras, T., Martins, A., Stanford, C., Escobar, C. O., Guenette, R., Stancari, M., et al. (2023). A method to characterize metalenses for light collection applications. J. Instrum., 18(9), T09004–11pp.
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Guadilla, V., Algora, A., Estienne, M., Fallot, M., Gelletly, W., Porta, A., et al. (2024). First measurements with a new fl-electron detector for spectral shape studies. J. Instrum., 19(2), P02027–21pp.
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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). Helium identification with LHCb. J. Instrum., 19(2), P02010–23pp.
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ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2024). Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data. J. Instrum., 19(2), P02009–58pp.
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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). Momentum scale calibration of the LHCb spectrometer. J. Instrum., 19(2), P02008–21pp.
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NEXT Collaboration(Mistry, K. et al), Carcel, S., Lopez-March, N., Martin-Albo, J., Novella, P., Querol, M., et al. (2024). Design, characterization and installation of the NEXT-100 cathode and electroluminescence regions. J. Instrum., 19(2), P02007–36pp.
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Andreotti, M. et al, Cervera-Villanueva, A., Garcia-Peris, M. a., Martin-Albo, J., Querol, M., Rocabado, J., et al. (2024). Cryogenic characterization of Hamamatsu HWB MPPCs for the DUNE photon detection system. J. Instrum., 19(1), T01007–27pp.
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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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