| Author |
Title |
Year  |
Publication |
Volume |
Pages |
| 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 |
| Folgado, M.G.; Sanz, V. |
Exploring the political pulse of a country using data science tools |
2022 |
Journal of Computational Social Science |
5 |
987-1000 |
| Khosa, C.K.; Sanz, V. |
On the Impact of the LHC Run 2 Data on General Composite Higgs Scenarios |
2022 |
Advances in High Energy Physics |
2022 |
8970837 - 13pp |
| Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. |
A deep Generative Artificial Intelligence system to predict species coexistence patterns |
2022 |
Methods in Ecology and Evolution |
13 |
1052-1061 |
| Cranmer, K. et al; Sanz, V. |
Publishing statistical models: Getting the most out of particle physics experiments |
2022 |
Scipost Physics |
12 |
037 - 55pp |
| Donini, A.; Enguita-Vileta, V.; Esser, F.; Sanz, V. |
Generalising Holographic Superconductors |
2022 |
Advances in High Energy Physics |
2022 |
1785050 - 19pp |
| Bagnaschi, E.; Ellis, J.; Madigan, M.; Mimasu, K.; Sanz, V.; You, T. |
SMEFT analysis of m(W) |
2022 |
Journal of High Energy Physics |
08 |
308 - 22pp |
| Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. |
Mapping the SMEFT to discoverable models |
2022 |
Journal of High Energy Physics |
09 |
229 - 34pp |
| 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 |
| Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
2022 |
Seismological Research Letters |
93 |
2529-2542 |
