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Arganda, E., Marcano, X., Martin Lozano, V., Medina, A. D., Perez, A. D., Szewc, M., et al. (2022). A method for approximating optimal statistical significances with machine-learned likelihoods. Eur. Phys. J. C, 82(11), 993–14pp.
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Perez Adan, D., Bahl, H., Grohsjean, A., Martin Lozano, V., Schwanenberger, C., & Weiglein, G. (2023). A new LHC search for dark matter produced via heavy Higgs bosons using simplified models. J. High Energy Phys., 08(8), 151–27pp.
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De Romeri, V., Kim, J. S., Martin Lozano, V., Rolbiecki, K., & Ruiz de Austri, R. (2016). Confronting dark matter with the diphoton excess from a parent resonance decay. Eur. Phys. J. C, 76(5), 262–13pp.
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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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Dreiner, H. K., Martin Lozano, V., Nangia, S., & Opferkuch, T. (2023). Lepton PDFs and multipurpose single-lepton searches at the LHC. Phys. Rev. D, 107(3), 035011–12pp.
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