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Alvarez, V., Herrero-Bosch, V., Esteve, R., Laing, A., Rodriguez, J., Querol, M., et al. (2019). The electronics of the energy plane of the NEXT-White detector. Nucl. Instrum. Methods Phys. Res. A, 917, 68–76.
Abstract: This paper describes the electronics of NEXT-White (NEW) detector PMT plane, a high pressure xenon TPC with electroluminescent amplification (HPXe-EL) currently operating at the Laboratorio Subterraneo de Canfranc (LSC) in Huesca, Spain. In NEXT-White the energy of the event is measured by a plane of photomultipliers (PMTs) located behind a transparent cathode. The PMTs are Hamamatsu R11410-10 chosen due to their low radioactivity. The electronics have been designed and implemented to fulfill strict requirements: an overall energy resolution below 1% and a radiopurity budget of 20 mBq unit(-1) in the chain of Bi-214. All the components and materials have been carefully screened to assure a low radioactivity level and at the same time meet the required front-end electronics specifications. In order to reduce low frequency noise effects and enhance detector safety a grounded cathode connection has been used for the PMTs. This implies an AC-coupled readout and baseline variations in the PMT signals. A detailed description of the electronics and a novel approach based on a digital baseline restoration to obtain a linear response and handle AC coupling effects is presented. The final PMT channel design has been characterized with linearity better than 0.4% and noise below 0.4 mV.
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ATLAS Collaboration(Aad, G. et al), Amoros, G., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Escobar, C., et al. (2011). Search for supersymmetry in pp collisions at sqrt(s)=7 TeV in final states with missing transverse momentum and b-jets. Phys. Lett. B, 701(4), 398–416.
Abstract: Results are presented of a search for supersymmetric particles in events with large missing transverse momentum and at least one heavy flavour jet candidate in root s = 7 TeV proton proton collisions. In a data sample corresponding to an integrated luminosity of 35 pb(-1) recorded by the ATLAS experiment at the Large Hadron Collider, no significant excess is observed with respect to the prediction for Standard Model processes. For R-parity conserving models in which sbottoms (stops) are the only squarks to appear in the gluino decay cascade, gluino masses below 590 GeV (520 GeV) are excluded at the 95% C.L. The results are also interpreted in an MSUGRA/CMSSM supersymmetry breaking scenario with tan beta = 40 and in an SO(10) model framework.
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BABAR Collaboration(Aubert, B. et al), Azzolini, V., Lopez-March, N., Martinez-Vidal, F., Milanes, D. A., & Oyanguren, A. (2013). The BABAR detector: Upgrades, operation and performance. Nucl. Instrum. Methods Phys. Res. A, 729, 615–701.
Abstract: The BABAR detector operated successfully at the PEP-Il asymmetric e(+) e(-) collider at the SLAC National Accelerator Laboratory from 1999 to 2008. This report covers upgrades, operation, and performance of the collider and the detector systems, as well as the trigger, online and offline computing, and aspects of event reconstruction since the beginning of data taking.
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Black, K. M. et al, & Zurita, J. (2024). Muon Collider Forum report. J. Instrum., 19(2), T02015–95pp.
Abstract: A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. The topic generated a lot of excitement in Snowmass meetings and continues to attract a large number of supporters, including many from the early career community. In light of this very strong interest within the US particle physics community, Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020. The Forum has been meeting on a monthly basis and organized several topical workshops dedicated to physics, accelerator technology, and detector R&D. Findings of the Forum are summarized in this report.
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Conde, D., Castillo, F. L., Escobar, C., García, C., Garcia Navarro, J. E., Sanz, V., et al. (2023). Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning. Space Weather, 21(11), e2023SW003474–27pp.
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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Fernandez Casani, A., Orduña, J. M., Sanchez, J., & Gonzalez de la Hoz, S. (2021). A Reliable Large Distributed Object Store Based Platform for Collecting Event Metadata. J. Grid Comput., 19(3), 39–19pp.
Abstract: The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed nature of the experiment data model is exploited by running jobs at over one hundred Grid data centers worldwide. Millions of files with petabytes of data are indexed, extracting a small quantity of metadata per event, that is conveyed with a data collection system in real time to a central Hadoop instance at CERN. After a successful first implementation based on a messaging system, some issues suggested performance bottlenecks for the challenging higher rates in next runs of the experiment. In this work we characterize the weaknesses of the previous messaging system, regarding complexity, scalability, performance and resource consumption. A new approach based on an object-based storage method was designed and implemented, taking into account the lessons learned and leveraging the ATLAS experience with this kind of systems. We present the experiment that we run during three months in the real production scenario worldwide, in order to evaluate the messaging and object store approaches. The results of the experiment show that the new object-based storage method can efficiently support large-scale data collection for big data environments like the next runs of the ATLAS experiment at the LHC.
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Garcfa-Barcelo, J. M., Melcon, A. A., Cuendis, S. A., Diaz-Morcillo, A., Gimeno, B., Kanareykin, A., et al. (2023). On the Development of New Tuning and Inter-Coupling Techniques Using Ferroelectric Materials in the Detection of Dark Matter Axions. IEEE Access, 11, 30360–30372.
Abstract: Tuning is an essential requirement for the search of dark matter axions employing haloscopes since its mass is not known yet to the scientific community. At the present day, most haloscope tuning systems are based on mechanical devices which can lead to failures due to the complexity of the environment in which they are used. However, the electronic tuning making use of ferroelectric materials can provide a path that is less vulnerable to mechanical failures and thus complements and expands current tuning systems. In this work, we present and design a novel technique for using the ferroelectric Potassium Tantalate (KTaO3 or KTO) material as a tuning element in haloscopes based on coupled microwave cavities. In this line, the structures used in the Relic Axion Detector Exploratory Setup (RADES) group are based on several cavities that are connected by metallic irises, which act as interresonator coupling elements. In this article, we also show how to use these KTaO3 films as interresonator couplings between cavities, instead of inductive or capacitive metallic windows used in the past. These two techniques represent a crucial upgrade over the current systems employed in the dark matter axions community, achieving a tuning range of 2.23% which represents a major improvement as compared to previous works (<0.1%) for the same class of tuning systems. The theoretical and simulated results shown in this work demonstrate the interest of the novel techniques proposed for the incorporation of this kind of ferroelectric media in multicavity resonant haloscopes in the search for dark matter axions.
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Hirn, J., Sanz, V., Garcia Navarro, J. E., Goberna, M., Montesinos-Navarro, A., Navarro-Cano, J. A., et al. (2024). Transfer learning of species co-occurrence patterns between plant communities. Ecol. Inform., 83, 102826–8pp.
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.
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Latonova, V. et al, Bernabeu, J., Lacasta, C., Solaz, C., & Soldevila, U. (2023). Characterization of the polysilicon resistor in silicon strip sensors for ATLAS inner tracker as a function of temperature, pre- and post-irradiation. Nucl. Instrum. Methods Phys. Res. A, 1050, 168119–5pp.
Abstract: The high luminosity upgrade of the Large Hadron Collider, foreseen for 2029, requires the replacement of the ATLAS Inner Detector with a new all-silicon Inner Tracker (ITk). The expected ultimate total integrated luminosity of 4000 fb(-1) means that the strip part of the ITk detector will be exposed to the total particle fluences and ionizing doses reaching the values of 1.6 center dot 10(15) MeVn(eq)/cm(2) and 0.66MGy, respectively, including a safety factor of 1.5. Radiation hard n(+)-in-p micro-strip sensors were developed by the ATLAS ITk strip collaboration and are produced by Hamamatsu Photonics K.K. The active area of each ITk strip sensor is delimited by the n-implant bias ring, which is connected to each individual n(+) implant strip by a polysilicon bias resistor. The total resistance of the polysilicon bias resistor should be within a specified range to keep all the strips at the same potential, prevent the signal discharge through the grounded bias ring and avoid the readout noise increase. While the polysilicon is a ubiquitous semiconductor material, the fluence and temperature dependence of its resistance is not easily predictable, especially for the tracking detector with the operational temperature significantly below the values typical for commercial microelectronics. Dependence of the resistance of polysilicon bias resistor on the temperature, as well as on the total delivered fluence and ionizing dose, was studied on the specially-designed test structures called ATLAS Testchips, both before and after their irradiation by protons, neutrons, and gammas to the maximal expected fluence and ionizing dose. The resistance has an atypical negative temperature dependence. It is different from silicon, which shows that the grain boundary has a significant contribution to the resistance. We discuss the contributions by parameterizing the activation energy of the polysilicon resistance as a function of the temperature for unirradiated and irradiated ATLAS Testchips.
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LHCb Collaboration(Aaij, R. et al), Martinez-Vidal, F., Oyanguren, A., Ruiz Valls, P., & Sanchez Mayordomo, C. (2014). Precision luminosity measurements at LHCb. J. Instrum., 9, P12005–91pp.
Abstract: Measuring cross-sections at the LHC requires the luminosity to be determined accurately at each centre-of-mass energy root s. In this paper results are reported from the luminosity calibrations carried out at the LHC interaction point 8 with the LHCb detector for root s = 2.76, 7 and 8TeV (proton-proton collisions) and for root s(NN) = 5TeV (proton-lead collisions). Both the “van der Meer scan” and “beam-gas imaging” luminosity calibration methods were employed. It is observed that the beam density profile cannot always be described by a function that is factorizable in the two transverse coordinates. The introduction of a two-dimensional description of the beams improves significantly the consistency of the results. For proton-proton interactions at root s = 8TeV a relative precision of the luminosity calibration of 1.47% is obtained using van der Meer scans and 1.43% using beam-gas imaging, resulting in a combined precision of 1.12%. Applying the calibration to the full data set determines the luminosity with a precision of 1.16%. This represents the most precise luminosity measurement achieved so far at a bunched-beam hadron collider.
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