| 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 |
| 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 |
| Kasieczka, G. et al; Sanz, V. |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
2021 |
Reports on Progress in Physics |
84 |
124201 - 64pp |
| Barenboim, G.; Hirn, J.; Sanz, V. |
Symmetry meets AI |
2021 |
Scipost Physics |
11 |
014 - 11pp |
