Author |
Title |
Year |
Publication |
Volume |
Pages  |
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 |