| |
Author  |
Title |
Year |
Publication |
Volume |
Pages |
Links |
|
|
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 |
|
|
|
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 |
|
|
|
Huang, F.; Sanz, V.; Shu, J.; Xue, X. |
LIGO as a probe of dark sectors |
2021 |
Physical Review D |
104 |
095001 - 9pp |
|
|
|
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 |
|
