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Author |
Donini, A.; Enguita-Vileta, V.; Esser, F.; Sanz, V. |
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Title  |
Generalising Holographic Superconductors |
Type |
Journal Article |
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Year |
2022 |
Publication |
Advances in High Energy Physics |
Abbreviated Journal |
Adv. High. Energy Phys. |
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Volume |
2022 |
Issue |
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Pages |
1785050 - 19pp |
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Abstract |
In this paper we propose a generalised holographic framework to describe superconductors. We first unify the description of s-, p-, and d-wave superconductors in a way that can be easily promoted to higher spin. Using a semianalytical procedure to compute the superconductor properties, we are able to further generalise the geometric description of the hologram beyond the AdS-Schwarzschild Black Hole paradigm and propose a set of higher-dimensional metrics which exhibit the same universal behaviour. We then apply this generalised description to study the properties of the condensate and the scaling of the critical temperature with the parameters of the higher-dimensional theory, which allows us to reproduce existing results in the literature and extend them to include a possible description of the newly observed f-wave superconducting systems. |
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Address |
[Donini, Andrea; Esser, Fabian] Univ Valencia CSIC, Inst Fis Corpuscular IFIC, E-46980 Valencia, Spain, Email: donini@ific.uv.es; |
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Publisher |
Hindawi Ltd |
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English |
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ISSN |
1687-7357 |
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Notes |
WOS:000817216300001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5277 |
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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 |
Type |
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 |
Fermionic UV models for neutral triple gauge boson vertices |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
07 |
Issue |
7 |
Pages |
275 - 28pp |
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Keywords |
Effective Field Theories; SMEFT; Specific BSM Phenomenology; Vector-Like Fermions |
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Abstract |
Searches for anomalous neutral triple gauge boson couplings (NTGCs) provide important tests for the gauge structure of the standard model. In SMEFT (“standard model effective field theory”) NTGCs appear only at the level of dimension-8 operators. While the phenomenology of these operators has been discussed extensively in the literature, renormalizable UV models that can generate these operators are scarce. In this work, we study a variety of extensions of the SM with heavy fermions and calculate their matching to d = 8 NTGC operators. We point out that the complete matching of UV models requires four different CP-conserving d = 8 operators and that the single CPC d = 8 operator, most commonly used by the experimental collaborations, does not describe all possible NTGC form factors. Despite stringent experimental constraints on NTGCs, limits on the scale of UV models are relatively weak, because their contributions are doubly suppressed (being d = 8 and 1-loop). We suggest a series of benchmark UV scenarios suitable for interpreting searches for NTGCs in the upcoming LHC runs, obtain their current limits and provide estimates for the expected sensitivity of the high-luminosity LHC. |
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Address |
[Cepedello, Ricardo] Univ Granada, Dept Fis Teor & Cosmos, Campus Fuentenueva, E-18071 Granada, Spain, Email: ricepe@ugr.es; |
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Publisher |
Springer |
Place of Publication |
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Language |
English |
Summary Language |
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Series Volume |
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ISSN |
1029-8479 |
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Notes |
WOS:001282227200003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6222 |
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Author |
Folgado, M.G.; Sanz, V. |
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Title |
Exploring the political pulse of a country using data science tools |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Computational Social Science |
Abbreviated Journal |
J. Comput. Soc. Sci. |
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Volume |
5 |
Issue |
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Pages |
987-1000 |
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Keywords |
Politics; Spain; Sentiment analysis; Artificial Intelligence; Machine learning; Neural networks; Natural Language Processing (NLP) |
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Abstract |
In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis. |
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Address |
[Folgado, Miguel G.; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Valencia 46980, Spain, Email: migarfol@upvnet.upv.es; |
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Publisher |
Springernature |
Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2432-2717 |
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Conference |
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Notes |
WOS:000742263500002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5077 |
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Author |
Khosa, C.K.; Mars, L.; Richards, J.; Sanz, V. |
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Title |
Convolutional neural networks for direct detection of dark matter |
Type |
Journal Article |
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Year |
2020 |
Publication |
Journal of Physics G |
Abbreviated Journal |
J. Phys. G |
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Volume |
47 |
Issue |
9 |
Pages |
095201 - 20pp |
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Keywords |
dark matter; dark matter detection; neural networks; xenon1T; WIMPs |
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Abstract |
The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%. |
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Address |
[Khosa, Charanjit K.; Mars, Lucy; Richards, Joel; Sanz, Veronica] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: charanjit.kaur@sussex.ac.uk; |
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Corporate Author |
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Thesis |
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Publisher |
Iop Publishing Ltd |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0954-3899 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000555607800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4485 |
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