| Author |
Title |
Year  |
Publication |
Volume |
Pages |
| 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 |
| Folgado, M.G.; Sanz, V. |
On the Interpretation of Nonresonant Phenomena at Colliders |
2021 |
Advances in High Energy Physics |
2021 |
2573471 - 12pp |
| 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 |
| Barenboim, G.; Hirn, J.; Sanz, V. |
Symmetry meets AI |
2021 |
Scipost Physics |
11 |
014 - 11pp |
| Khosa, C.K.; Sanz, V.; Soughton, M. |
Using machine learning to disentangle LHC signatures of Dark Matter candidates |
2021 |
Scipost Physics |
10 |
151 - 26pp |
| Huang, F.; Sanz, V.; Shu, J.; Xue, X. |
LIGO as a probe of dark sectors |
2021 |
Physical Review D |
104 |
095001 - 9pp |
| Bonilla, J.; Brivio, I.; Gavela, M.B.; Sanz, V. |
One-loop corrections to ALP couplings |
2021 |
Journal of High Energy Physics |
11 |
168 - 57pp |
| 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 |
| Khosa, C.K.; Mars, L.; Richards, J.; Sanz, V. |
Convolutional neural networks for direct detection of dark matter |
2020 |
Journal of Physics G |
47 |
095201 - 20pp |
