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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 |
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Journal Article |
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Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
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Volume |
93 |
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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 @ |
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5500 |
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Esser, F.; Madigan, M.; Sanz, V.; Ubiali, M. |
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On the coupling of axion-like particles to the top quark |
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Journal Article |
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Year |
2023 |
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Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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09 |
Issue |
9 |
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063 - 39pp |
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In this paper we explore the coupling of a light axion-like particle (ALP) to top quarks. We use high-energy LHC probes, and examine both the direct probe to this coupling in associated production of a top-pair with an ALP, and the indirect probe through loop-induced gluon fusion to an ALP leading to top pairs. Using the latest LHC Run II data, we provide the best limit on this coupling. We also compare these limits with those obtained from loop-induced couplings in diboson final states, finding that the +MET channel is the best current handle on this coupling. |
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no |
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yes |
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yes |
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IFIC @ pastor @ |
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6083 |
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Escudero, M.; Rius, N.; Sanz, V. |
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Title |
Sterile neutrino portal to Dark Matter I: the U(1)(B-L) case |
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Journal Article |
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2017 |
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Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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02 |
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2 |
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045 - 27pp |
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Beyond Standard Model; Neutrino Physics |
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In this paper we explore the possibility that the sterile neutrino and Dark Matter sectors in the Universe have a common origin. We study the consequences of this assumption in the simple case of coupling the dark sector to the Standard Model via a global U(1)(B-L), broken down spontaneously by a dark scalar. This dark scalar provides masses to the dark fermions and communicates with the Higgs via a Higgs portal coupling. We find an interesting interplay between Dark Matter annihilation to dark scalars – the CP-even that mixes with the Higgs and the CP-odd which becomes a Goldstone boson, the Majoron and heavy neutrinos, as well as collider probes via the coupling to the Higgs. Moreover, Dark Matter annihilation into sterile neutrinos and its subsequent decay to gauge bosons and quarks, charged leptons or neutrinos lead to indirect detection signatures which are close to current bounds on the gamma ray flux from the galactic center and dwarf galaxies. |
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[Escudero, Miguel; Rius, Nuria] Univ Valencia, Dept Fis Teor, CSIC, C Catedrat Jose Beltran 2, E-46980 Paterna, Spain, Email: miguel.escudero@ific.uv.es; |
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Springer |
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English |
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1029-8479 |
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WOS:000394747600008 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3018 |
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Author |
Escudero, M.; Rius, N.; Sanz, V. |
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Title |
Sterile neutrino portal to Dark Matter II: exact dark symmetry |
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Journal Article |
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Year |
2017 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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77 |
Issue |
6 |
Pages |
397 - 11pp |
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We analyze a simple extension of the standard model (SM) with a dark sector composed of a scalar and a fermion, both singlets under the SM gauge group but charged under a dark sector symmetry group. Sterile neutrinos, which are singlets under both groups, mediate the interactions between the dark sector and the SM particles, and generate masses for the active neutrinos via the seesaw mechanism. We explore the parameter space region where the observed Dark Matter relic abundance is determined by the annihilation into sterile neutrinos, both for fermion and scalar Dark Matter particles. The scalar Dark Matter case provides an interesting alternative to the usual Higgs portal scenario. We also study the constraints from direct Dark Matter searches and the prospects for indirect detection via sterile neutrino decays to leptons, which may be able to rule out Dark Matter masses below and around 100 GeV. |
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[Escudero, Miguel; Rius, Nuria] Univ Valencia, CSIC, Dept Fis Teor, C Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: miguel.escudero@ific.uv.es; |
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Springer |
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English |
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1434-6044 |
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Notes |
WOS:000403504200002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3171 |
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Permanent link to this record |
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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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[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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Iop Publishing Ltd |
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English |
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0954-3899 |
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Notes |
WOS:000555607800001 |
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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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Permanent link to this record |