| Records |
| Author |
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
| Title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
Type |
Journal Article |
| Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
| 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 |
International Collaboration  |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5500 |
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| Author |
Gomez Ambrosio, R.; ter Hoeve, J.; Madigan, M.; Rojo, J.; Sanz, V. |
| Title |
Unbinned multivariate observables for global SMEFT analyses from machine learning |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
03 |
Issue |
3 |
Pages |
033 - 66pp |
| Keywords |
SMEFT; Higgs Properties |
| Abstract |
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits. |
| Address |
[Ambrosio, Raquel Gomez] Univ Milano Bicocca, Dipartimento Fis G Occhialini, Piazza Sci 3, I-20126 Milan, Italy, Email: raquel.gomezambrosio@unito.it; |
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Springer |
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English |
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1029-8479 |
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WOS:000946004000003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5501 |
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| Author |
Khosa, C.K.; Sanz, V. |
| Title |
Anomaly Awareness |
Type |
Journal Article |
| Year |
2023 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
| Volume |
15 |
Issue |
2 |
Pages |
053 - 24pp |
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| Abstract |
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies. |
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[Khosa, Charanjit K.] Univ Manchester, Dept Phys & Astron, Manchester M13 9PL, England |
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Scipost Foundation |
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English |
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2542-4653 |
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WOS:001048488200002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5610 |
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| Author |
Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. |
| Title |
SMEFT goes dark: Dark Matter models for four-fermion operators |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
09 |
Issue |
9 |
Pages |
081 - 47pp |
| Keywords |
SMEFT; Dark Matter at Colliders; Specific BSM Phenomenology |
| Abstract |
We study ultra-violet completions for d = 6 four-fermion operators in the standard model effective field theory (SMEFT), focusing on models that contain cold dark matter candidates. Via a diagrammatic method, we generate systematically lists of possible UV completions, with the aim of providing sets of models, which are complete under certain, well specified assumptions. Within these lists of models we rediscover many known DM models, as diverse as R-parity conserving supersymmetry or the scotogenic neutrino mass model. Our lists, however, also contain many new constructions, which have not been studied in the literature so far. We also briefly discuss how our DM models could be constrained by reinterpretations of LHC searches and the prospects for HL-LHC and future lepton colliders. |
| Address |
[Cepedello, Ricardo] Univ Wurzburg, Inst Theoret Phys & Astrophys, D-97074 Wurzburg, Germany, Email: ricardo.cepedello@physik.uni-wuerzburg.de; |
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Springer |
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English |
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1029-8479 |
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WOS:001067194100002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5688 |
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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. |
| Title |
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
Type |
Journal Article |
| Year |
2023 |
Publication |
Space Weather |
Abbreviated Journal |
Space Weather |
| Volume |
21 |
Issue |
11 |
Pages |
e2023SW003474 - 27pp |
| Keywords |
geomagnetic storms; deep learning; forecasting; SYM-H; uncertainties; hyper-parameter optimization |
| 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. |
| 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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WOS:001104189700001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5804 |
| Permanent link to this record |
