Ruhr, F. et al, Escobar, C., & Miñano, M. (2020). Testbeam studies of barrel and end-cap modules for the ATLAS ITk strip detector before and after irradiation. Nucl. Instrum. Methods Phys. Res. A, 979, 164430–6pp.
Abstract: In order to cope with the occupancy and radiation doses expected at the High-Luminosity LHC, the ATLAS experiment will replace its Inner Detector with an all-silicon Inner Tracker (ITk), consisting of pixel and strip subsystems. In the last two years, several prototype ITk strip modules have been tested using beams of high energy electrons produced at the DESY-II testbeam facility. Tracking was provided by EUDET telescopes. The modules tested are built from two sensor types: the rectangular ATLAS17LS, which will be used in the outer layers of the central barrel region of the detector, and the annular ATLAS12EC, which will be used in the innermost ring (R0) of the forward region. Additionally, a structure with two RO modules positioned back-to-back has been measured, demonstrating space point reconstruction using the stereo angle of the strips. Finally, one barrel and one RO module have been measured after irradiation to 40% beyond the expected end-of-lifetime fluence. The data obtained allow for thorough tests of the module performance, including charge collection, noise occupancy, detection efficiency, and tracking performance. The results give confidence that the ITk strip detector will meet the requirements of the ATLAS experiment.
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Martins, A., da Mota, A. F., Stanford, C., Contreras, T., Martin-Albo, J., Kish, A., et al. (2024). Simple strategy for the simulation of axially symmetric large-area metasurfaces. J. Opt. Soc. Am. B, 41(5), 1261–1269.
Abstract: Metalenses are composed of nanostructures for focusing light and have been widely explored in many exciting applications. However, their expanding dimensions pose simulation challenges. We propose a method to simulate metalenses in a timely manner using vectorial wave and ray tracing models. We sample the metalens's radial phase gradient and locally approximate the phase profile by a linear phase response. Each sampling point is modeled as a binary blazed grating, employing the chosen nanostructure, to build a transfer function set. The metalens transmission or reflection is then obtained by applying the corresponding transfer function to the incoming field on the regions surrounding each sampling point. Fourier optics is used to calculate the scattered fields under arbitrary illumination for the vectorial wave method, and a Monte Carlo algorithm is used in the ray tracing formalism. We validated our method against finite -difference time domain simulations at 632 nm, and we were able to simulate metalenses larger than 3000 wavelengths in diameter on a personal computer.
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Hara, K. et al, Escobar, C., Garcia, C., Lacasta, C., Miñano, M., & Soldevila, U. (2020). Charge collection study with the ATLAS ITk prototype silicon strip sensors ATLAS17LS. Nucl. Instrum. Methods Phys. Res. A, 983, 164422–6pp.
Abstract: The inner tracker of the ATLAS detector is scheduled to be replaced by a completely new silicon-based inner tracker (ITk) for the Phase-II of the CERN LHC (HL-LHC). The silicon strip detector covers the volume 40 < R < 100 cm in the radial and vertical bar z vertical bar <300 cm in the longitudinal directions. The silicon sensors for the detector will be fabricated using the n(+)-on-p 6-inch wafer technology, for a total of 22,000 wafers. Intensive studies were carried out on the final prototype sensors ATLAS17LS fabricated by Hamamatsu Photonics (HPK). The charge collection properties were examined using penetrating Sr-90 beta-rays and the ALIBAVA fast readout system for the miniature sensors of 1 cm xl cm in area. The samples were irradiated by protons in the 27 MeV Birmingham Cyclotron, the 70 MeV CYRIC at Tohoku University, and the 24 GeV CERN-PS, and by neutrons at Ljubljana TAIGA reactor for fluence values up to 2 x 10(15) n(eq)/cm(2). The change in the charge collection with fluence was found to be similar to the previous prototype ATLAS12, and acceptable for the ITk. Sensors with two active thicknesses, 300 μm (standard) and 240 μm (thin), were compared and the difference in the charge collection was observed to be small for bias voltages up to 500 V. Some samples were also irradiated with gamma radiation up to 2 MGy, and the full depletion voltage was found to decrease with the dose. This was caused by the Compton electrons due to the( 60)Co gamma radiation. To summarize, the design of the ATLAS17LS and technology for its fabrication have been verified for implementation in the ITk. We are in the stage of sensor pre-production with the first sensors already delivered in January of 2020.
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Contreras, T., Martins, A., Stanford, C., Escobar, C. O., Guenette, R., Stancari, M., et al. (2023). A method to characterize metalenses for light collection applications. J. Instrum., 18(9), T09004–11pp.
Abstract: Metalenses and metasurfaces are promising emerging technologies that could improve light collection in light collection detectors, concentrating light on small area photodetectors such as silicon photomultipliers. Here we present a detailed method to characterize metalenses to assess their efficiency at concentrating monochromatic light coming from a wide range of incidence angles, not taking into account their imaging quality.
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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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