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Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
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Title |
A simple guide from machine learning outputs to statistical criteria in particle physics |
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Journal Article |
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Year |
2022 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
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Volume |
5 |
Issue |
4 |
Pages |
050 - 31pp |
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Abstract  |
In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-pT hadronic activity, and boosted Higgs in association with a massive vector boson. |
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Address |
[Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.ac.uk; |
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Scipost Foundation |
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English |
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WOS:000929724800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5475 |
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Author |
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
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Title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
Type |
Journal Article |
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Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
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Volume |
93 |
Issue |
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Pages |
2529-2542 |
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Abstract |
Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures. |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
Serial |
5500 |
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Author |
Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. |
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Title |
A deep Generative Artificial Intelligence system to predict species coexistence patterns |
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Journal Article |
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Year |
2022 |
Publication |
Methods in Ecology and Evolution |
Abbreviated Journal |
Methods Ecol. Evol. |
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Volume |
13 |
Issue |
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Pages |
1052-1061 |
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Keywords |
artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders |
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Abstract |
Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge. |
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Address |
[Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es |
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Publisher |
Wiley |
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English |
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ISSN |
2041-210x |
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WOS:000765239700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5155 |
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Author |
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. |
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Title |
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
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Journal Article |
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Year |
2023 |
Publication |
Space Weather |
Abbreviated Journal |
Space Weather |
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Volume |
21 |
Issue |
11 |
Pages |
e2023SW003474 - 27pp |
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Keywords |
geomagnetic storms; deep learning; forecasting; SYM-H; uncertainties; hyper-parameter optimization |
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Abstract |
Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests. |
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Address |
[Conde, D.; Escobar, C.; Garcia, C.; Garcia, J. E.; Sanz, V.; Zaldivar, B.] Univ Valencia, CSIC, Ctr Mixto, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Daniel.Conde@ific.uv.es |
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Publisher |
Amer Geophysical Union |
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English |
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Notes |
WOS:001104189700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5804 |
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Author |
Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. |
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Title |
Mapping the SMEFT to discoverable models |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
09 |
Issue |
9 |
Pages |
229 - 34pp |
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Keywords |
SMEFT; Other Weak Scale BSM Models |
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Abstract |
The matching of specific new physics scenarios onto the SMEFT framework is a well-understood procedure. The inverse problem, the matching of the SMEFT to UV scenarios, is more difficult and requires the development of new methods to perform a systematic exploration of models. In this paper we use a diagrammatic technique to construct in an automated way a complete set of possible UV models (given certain, well specified assumptions) that can produce specific groups of SMEFT operators, and illustrate its use by generating models with no tree-level contributions to four-fermion (4F) operators. Those scenarios, which only contribute to 4F at one-loop order, can contain relatively light particles that could be discovered at the LHC in direct searches. For this class of models, we find an interesting interplay between indirect SMEFT and direct searches. We discuss some examples on how this interplay would look like when combining low-energy observables with the SMEFT Higgs-fermion analyses and searches for resonance at the LHC. |
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Address |
[Cepedello, Ricardo] Univ Wurzburg, Inst Theoret Phys & Astrophys, Emil Hilb Weg 22, D-97074 Wurzburg, Germany, Email: ricardo.cepedello@physik.uni-wuerzburg.de; |
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Publisher |
Springer |
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English |
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Edition |
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ISSN |
1029-8479 |
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Notes |
WOS:000861474500009 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5361 |
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