| Author |
Title |
Year |
Publication |
Volume |
Pages |
| 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 |
| Ellis, J.; Madigan, M.; Mimasu, K.; Sanz, V.; You, T. |
Top, Higgs, diboson and electroweak fit to the Standard Model effective field theory |
2021 |
Journal of High Energy Physics |
04 |
279 - 78pp |
| 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 |
| Lessa, A.; Sanz, V. |
Going beyond Top EFT |
2024 |
Journal of High Energy Physics |
04 |
107 - 29pp |
| Escudero, M.; Rius, N.; Sanz, V. |
Sterile neutrino portal to Dark Matter II: exact dark symmetry |
2017 |
European Physical Journal C |
77 |
397 - 11pp |
