Author  |
Title |
Year |
Publication |
Volume |
Pages |
| Hirn, J.; Sanz, V.; Garcia Navarro, J.E.; Goberna, M.; Montesinos-Navarro, A.; Navarro-Cano, J.A.; Sanchez-Martin, R.; Valiente-Banuet, A.; Verdu, M. |
Transfer learning of species co-occurrence patterns between plant communities |
2024 |
Ecological Informatics |
83 |
102826 - 8pp |
| 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 |
| Khosa, C.K.; Sanz, V. |
Anomaly Awareness |
2023 |
Scipost Physics |
15 |
053 - 24pp |
| 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 |
| 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 |
| 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 |
| LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V. |
Reinterpretation of LHC results for new physics: status and recommendations after run 2 |
2020 |
Scipost Physics |
9 |
022 - 45pp |
