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Bertone, G., Cerdeño, D. G., Fornasa, M., Pieri, L., Ruiz de Austri, R., & Trotta, R. (2012). Complementarity of indirect and accelerator dark matter searches. Phys. Rev. D, 85(5), 055014–10pp.
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Gunion, J. F., Lopez-Fogliani, D. E., Roszkowski, L., Ruiz de Austri, R., & Varley, T. A. (2011). Next-to-minimal supersymmetric model Higgs scenarios for partially universal GUT scale boundary conditions. Phys. Rev. D, 84(5), 055026–17pp.
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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., Mamuzic, J., Mitsou, V. A., Ruiz de Austri, R., et al. (2017). Search for Magnetic Monopoles with the MoEDAL Forward Trapping Detector in 13 TeV Proton-Proton Collisions at the LHC. Phys. Rev. Lett., 118(6), 061801–6pp.
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MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Mamuzic, J., Mitsou, V. A., Papavassiliou, J., Ruiz de Austri, R., et al. (2021). First Search for Dyons with the Full MoEDAL Trapping Detector in 13 TeV pp Collisions. Phys. Rev. Lett., 126(7), 071801–7pp.
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Lara, I., Lopez-Fogliani, D. E., Muñoz, C., Nagata, N., Otono, H., & Ruiz de Austri, R. (2018). Looking for the left sneutrino LSP with displaced-vertex searches. Phys. Rev. D, 98(7), 075004–17pp.
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Domingo, F., Kim, J. S., Martin Lozano, V., Martin-Ramiro, P., & Ruiz de Austri, R. (2020). Confronting the neutralino and chargino sector of the NMSSM with the multilepton searches at the LHC. Phys. Rev. D, 101(7), 075010–29pp.
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Pato, M., Baudis, L., Bertone, G., Ruiz de Austri, R., Strigari, L. E., & Trotta, R. (2011). Complementarity of dark matter direct detection targets. Phys. Rev. D, 83(8), 083505–11pp.
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Kim, J. S., Rolbiecki, K., Ruiz de Austri, R., Tattersall, J., & Weber, T. (2016). Prospects for natural SUSY. Phys. Rev. D, 94(9), 095013–19pp.
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Dorigo, T. et al, Ramos, A., & Ruiz de Austri, R. (2023). Toward the end-to-end optimization of particle physics instruments with differentiable programming. Rev. Phys., 10, 100085– pp.
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Begone, G., Deisenroth, M. P., Kim, J. S., Liem, S., Ruiz de Austri, R., & Welling, M. (2019). Accelerating the BSM interpretation of LHC data with machine learning. Phys. Dark Universe, 24, 100293–5pp.
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