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Author Huang, F.; Sanz, V.; Shu, J.; Xue, X. url  doi
openurl 
  Title LIGO as a probe of dark sectors Type Journal Article
  Year 2021 Publication Physical Review D Abbreviated Journal Phys. Rev. D  
  Volume 104 Issue 10 Pages (down) 095001 - 9pp  
  Keywords  
  Abstract We show how current LIGO data is able to probe interesting theories beyond the Standard Model, particularly dark sectors where a dark Higgs boson triggers symmetry breaking via a first-order phase transition. We use publicly available LIGO O2 data to illustrate how these sectors, even if disconnected from the Standard Model, can be probed by gravitational wave detectors. We link the LIGO measurements with the model content and mass scale of the dark sector, finding that current O2 data are testing a broad set of scenarios that can be mapped into many different types of dark-sector models where the breaking of SU(N) theories with Nf fermions is triggered by a dark Higgs boson at scales ? similar or equal to 108-109 GeV with reasonable parameters for the scalar potential.  
  Address [Huang, Fei; Shu, Jing; Xue, Xiao] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China, Email: huangf4@uci.edu;  
  Corporate Author Thesis  
  Publisher Amer Physical Soc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2470-0010 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000716446500001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5021  
Permanent link to this record
 

 
Author Sanchis-Lozano, M.A.; Sanz, V. url  doi
openurl 
  Title Observable imprints of primordial gravitational waves on the temperature anisotropies of the cosmic microwave background Type Journal Article
  Year 2024 Publication Physical Review D Abbreviated Journal Phys. Rev. D  
  Volume 109 Issue 6 Pages (down) 063529 - 11pp  
  Keywords  
  Abstract We examine the contribution of tensor modes, in addition to the dominant scalar ones, on the temperature anisotropies of the cosmic microwave background (CMB). To this end, we analyze in detail the temperature two -point angular correlation function C(Theta) from the Planck 2018 dataset, focusing on large angles (Theta greater than or similar to 120 degrees) corresponding to small l multipoles. A hierarchical set of infrared cutoffs are naturally introduced to the scalar and tensor power spectra of the CMB by invoking an extra Kaluza-Klein spatial dimension compactifying at about the grand unified theory scale between the Planck epoch and the start of inflation. We associate this set of lower scalar and tensor cutoffs with the parity of the multipole expansion of the C(Theta) function. By fitting the Planck 2018 data we compute the multipole coefficients, thereby reproducing the well-known odd -parity preference in angular correlations seen by all three satellite missions: Cosmic Background Explorer, WMAP, and Planck. Our fits improve significantly once tensor modes are included in the analysis, hence providing a hint of the imprints of primordial gravitational waves on the temperature correlations observed in the CMB today. To conclude, we suggest a relationship between, on the one hand, the lack of (positive) large -angle correlations and the odd -parity dominance in the CMB and, on the other hand, the effect of primordial gravitational waves on the CMB temperature anisotropies.  
  Address [Sanchis-Lozano, Miguel -Angel; Sanz, Veronica] Univ Valencia, Dept Fis Teor, CSIC, Valencia 46100, Spain, Email: miguel.angel.sanchis@ific.uv.es;  
  Corporate Author Thesis  
  Publisher Amer Physical Soc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2470-0010 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001195716600006 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 6038  
Permanent link to this record
 

 
Author Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. doi  openurl
  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 Pages (down) 2529-2542  
  Keywords  
  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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5500  
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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. doi  openurl
  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 (down) 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  
  Corporate Author Thesis  
  Publisher Amer Geophysical Union Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001104189700001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5804  
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Author Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. url  doi
openurl 
  Title A deep Generative Artificial Intelligence system to predict species coexistence patterns Type Journal Article
  Year 2022 Publication Methods in Ecology and Evolution Abbreviated Journal Methods Ecol. Evol.  
  Volume 13 Issue Pages (down) 1052-1061  
  Keywords artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders  
  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.  
  Address [Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es  
  Corporate Author Thesis  
  Publisher Wiley Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2041-210x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000765239700001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5155  
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