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Author |
Title |
Year |
Publication  |
Volume |
Pages |
Links |
|
|
Conde, D.; Castillo, F.L.; Escobar, C.; García, C.; Garcia Navarro, J.E.; Sanz, V.; Zaldívar, B.; Curto, J.J.; Marsal, S.; Torta, J.M. |
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
2023 |
Space Weather |
21 |
e2023SW003474 - 27pp |
|
|
|
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 |
|
|
|
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 |
|
|
|
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 |
|
|
|
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 |
|
|
|
Cranmer, K. et al; Sanz, V. |
Publishing statistical models: Getting the most out of particle physics experiments |
2022 |
Scipost Physics |
12 |
037 - 55pp |
|
|
|
Khosa, C.K.; Sanz, V. |
Anomaly Awareness |
2023 |
Scipost Physics |
15 |
053 - 24pp |
|
|
|
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 |
|
|
|
Huang, F.; Sanz, V.; Shu, J.; Xue, X. |
LIGO as a probe of dark sectors |
2021 |
Physical Review D |
104 |
095001 - 9pp |
|
