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Author Title Year Publication Volume Pages (down)
Khosa, C.K.; Sanz, V.; Soughton, M. Using machine learning to disentangle LHC signatures of Dark Matter candidates 2021 Scipost Physics 10 151 - 26pp
Lessa, A.; Sanz, V. Going beyond Top EFT 2024 Journal of High Energy Physics 04 107 - 29pp
Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. Faking ZZZ vertices at the LHC 2024 Journal of High Energy Physics 12 098 - 20pp
Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. SMEFT goes dark: Dark Matter models for four-fermion operators 2023 Journal of High Energy Physics 09 081 - 47pp
Esser, F.; Madigan, M.; Sanz, V.; Ubiali, M. On the coupling of axion-like particles to the top quark 2023 Journal of High Energy Physics 09 063 - 39pp
Khosa, C.K.; Sanz, V. Anomaly Awareness 2023 Scipost Physics 15 053 - 24pp
Khosa, C.K.; Sanz, V.; Soughton, M. A simple guide from machine learning outputs to statistical criteria in particle physics 2022 Scipost Physics Core 5 050 - 31pp
Escudero, M.; Rius, N.; Sanz, V. Sterile neutrino portal to Dark Matter I: the U(1)(B-L) case 2017 Journal of High Energy Physics 02 045 - 27pp
Cranmer, K. et al; Sanz, V. Publishing statistical models: Getting the most out of particle physics experiments 2022 Scipost Physics 12 037 - 55pp
Gomez Ambrosio, R.; ter Hoeve, J.; Madigan, M.; Rojo, J.; Sanz, V. Unbinned multivariate observables for global SMEFT analyses from machine learning 2023 Journal of High Energy Physics 03 033 - 66pp