Records |
Author |
Khosa, C.K.; Sanz, V. |
Title |
On the Impact of the LHC Run 2 Data on General Composite Higgs Scenarios |
Type |
Journal Article |
Year |
2022 |
Publication |
Advances in High Energy Physics |
Abbreviated Journal |
Adv. High. Energy Phys. |
Volume |
2022 |
Issue |
|
Pages  |
8970837 - 13pp |
Keywords |
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Abstract |
We study the impact of Run 2 LHC data on general composite Higgs scenarios, where nonlinear effects, mixing with additional scalars, and new fermionic degrees of freedom could simultaneously contribute to the modification of Higgs properties. We obtain new experimental limits on the scale of compositeness, the mixing with singlets and doublets with the Higgs, and the mass and mixing angle of top-partners. We also show that for scenarios where new fermionic degrees of freedom are involved in electroweak symmetry breaking, there is an interesting interplay among Higgs coupling measurements, boosted Higgs properties, SMEFT global analyses, and direct searches for single and double production of vector-like quarks. |
Address |
[Khosa, Charanjit K.] Univ Genoa, Dipartimento Fis, Via Dodecaneso 33, I-16146 Genoa, Italy, Email: khosacharanjit@gmail.com; |
Corporate Author |
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Thesis |
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Publisher |
Hindawi 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 |
1687-7357 |
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:000766325700001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5153 |
Permanent link to this record |
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Author |
Folgado, M.G.; Sanz, V. |
Title |
On the Interpretation of Nonresonant Phenomena at Colliders |
Type |
Journal Article |
Year |
2021 |
Publication |
Advances in High Energy Physics |
Abbreviated Journal |
Adv. High. Energy Phys. |
Volume |
2021 |
Issue |
|
Pages  |
2573471 - 12pp |
Keywords |
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Abstract |
With null results in resonance searches at the LHC, the physics potential focus is now shifting towards the interpretation of nonresonant phenomena. An example of such shift is the increased popularity of the EFT programme. We can embark on such programme owing to the good integrated luminosity and an excellent understanding of the detectors, which will allow these searches to become more intense as the LHC continues. In this paper, we provide a framework to perform this interpretation in terms of a diverse set of scenarios, including (1) generic heavy new physics described at low energies in terms of a derivative expansion, such as in the EFT approach; (2) very light particles with derivative couplings, such as axions or other light pseudo-Goldstone bosons; and (3) the effect of a quasicontinuum of resonances, which can come from a number of strongly coupled theories, extradimensional models, clockwork set-ups, and their deconstructed cousins. These scenarios are not equivalent despite all nonresonance, although the matching among some of them is possible, and we provide it in this paper. |
Address |
[Folgado, Miguel G.; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, E-46980 Valencia, Spain, Email: migarfol@ific.uv.es |
Corporate Author |
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Thesis |
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Publisher |
Hindawi 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 |
1687-7357 |
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:000636258800001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
4775 |
Permanent link to this record |
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Author |
Donini, A.; Enguita-Vileta, V.; Esser, F.; Sanz, V. |
Title |
Generalising Holographic Superconductors |
Type |
Journal Article |
Year |
2022 |
Publication |
Advances in High Energy Physics |
Abbreviated Journal |
Adv. High. Energy Phys. |
Volume |
2022 |
Issue |
|
Pages  |
1785050 - 19pp |
Keywords |
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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. |
Address |
[Donini, Andrea; Esser, Fabian] Univ Valencia CSIC, Inst Fis Corpuscular IFIC, E-46980 Valencia, Spain, Email: donini@ific.uv.es; |
Corporate Author |
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Thesis |
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Publisher |
Hindawi 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 |
1687-7357 |
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:000817216300001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5277 |
Permanent link to this record |
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Author |
Kasieczka, G. et al; Sanz, V. |
Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
Type |
Journal Article |
Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
Volume |
84 |
Issue |
12 |
Pages  |
124201 - 64pp |
Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
Abstract |
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. |
Address |
[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
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 |
0034-4885 |
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:000727698500001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5039 |
Permanent link to this record |
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Author |
Perez-Curbelo, J.; Roser, J.; Muñoz, E.; Barrientos, L.; Sanz, V.; Llosa, G. |
Title |
Improving Compton camera imaging of multi-energy radioactive sources by using machine learning algorithms for event selection |
Type |
Journal Article |
Year |
2025 |
Publication |
Radiation Physics and Chemistry |
Abbreviated Journal |
Radiat. Phys. Chem. |
Volume |
226 |
Issue |
|
Pages  |
112166 - 11pp |
Keywords |
Compton cameras imaging; Event selection; Neural networks; Image reconstruction |
Abstract |
Event selection and background reduction for Compton camera imaging of multi-energy radioactive sources has been performed by employing neural networks. A Compton camera prototype with detectors made of LaBr3 crystals coupled to silicon photomultiplier arrays was used to acquire experimental data from a circular array of Na-22 sources. The prototype and two arrays of Na-22 sources were simulated with GATE v8.2 Monte Carlo code, to obtain data for neural network training. Neural network models were trained on simulated data for event classification. The optimum models were found by using Weights & Biases platform tools. The trained models were used to classify simulated and real data for selecting signal events and rejecting background prior to image reconstruction. The models performed well on simulated data. The image obtained with experimental data showed an improvement with respect to event selection with energy cuts. The method is promising for Compton camera imaging of multi-energy radioactive sources. |
Address |
[Perez-Curbelo, J.; Roser, J.; Munoz, E.; Barrientos, L.; Sanz, V.; Llosa, G.] CSIC UV, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Javier.Perez.Curbelo@ific.uv.es |
Corporate Author |
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Thesis |
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Publisher |
Pergamon-Elsevier Science 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 |
0969-806x |
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:001325220200001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
no |
Call Number |
IFIC @ pastor @ |
Serial |
6269 |
Permanent link to this record |
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Author |
Hirn, J.; Sanz, V.; Garcia Navarro, J.E.; Goberna, M.; Montesinos-Navarro, A.; Navarro-Cano, J.A.; Sanchez-Martin, R.; Valiente-Banuet, A.; Verdu, M. |
Title |
Transfer learning of species co-occurrence patterns between plant communities |
Type |
Journal Article |
Year |
2024 |
Publication |
Ecological Informatics |
Abbreviated Journal |
Ecol. Inform. |
Volume |
83 |
Issue |
|
Pages  |
102826 - 8pp |
Keywords |
Generative artificial intelligence; Patchy vegetation; Plant communities; Restoration ecology; Species co-occurrence; Variational autoencoders |
Abstract |
Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and Me<acute accent>xico. Time period: 2016-2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates. |
Address |
[Hirn, Johannes; Montesinos-Navarro, Alicia; Sanchez-Martin, Ricardo; Verdu, Miguel] Univ Valencia Generalitat Valenciana, Ctr Invest Desertificac CIDE, CSIC, Valencia, Spain |
Corporate Author |
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Thesis |
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Publisher |
Elsevier |
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 |
1574-9541 |
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:001327519900001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
6278 |
Permanent link to this record |
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Author |
Khosa, C.K.; Mars, L.; Richards, J.; Sanz, V. |
Title |
Convolutional neural networks for direct detection of dark matter |
Type |
Journal Article |
Year |
2020 |
Publication |
Journal of Physics G |
Abbreviated Journal |
J. Phys. G |
Volume |
47 |
Issue |
9 |
Pages  |
095201 - 20pp |
Keywords |
dark matter; dark matter detection; neural networks; xenon1T; WIMPs |
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%. |
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; |
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 |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
4485 |
Permanent link to this record |
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Author |
Huang, F.; Sanz, V.; Shu, J.; Xue, X. |
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  |
095001 - 9pp |
Keywords |
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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; |
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Thesis |
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Publisher |
Amer Physical Soc |
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 |
2470-0010 |
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:000716446500001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5021 |
Permanent link to this record |
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Author |
Sanchis-Lozano, M.A.; Sanz, V. |
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  |
063529 - 11pp |
Keywords |
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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; |
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Publisher |
Amer Physical Soc |
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 |
2470-0010 |
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:001195716600006 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
no |
Call Number |
IFIC @ pastor @ |
Serial |
6038 |
Permanent link to this record |
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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. |
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  |
2529-2542 |
Keywords |
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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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Notes |
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Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5500 |
Permanent link to this record |