Gonzalez-Sevilla, S. et al, Bernabeu Verdu, J., Civera, J. V., Garcia, C., Lacasta, C., Marco, R., et al. (2014). A double-sided silicon micro-strip Super-Module for the ATLAS Inner Detector upgrade in the High-Luminosity LHC. J. Instrum., 9, P02003–37pp.
Abstract: The ATLAS experiment is a general purpose detector aiming to fully exploit the discovery potential of the Large Hadron Collider (LHC) at CERN. It is foreseen that after several years of successful data-taking, the LHC physics programme will be extended in the so-called High-Luminosity LHC, where the instantaneous luminosity will be increased up to 5 x 10(34) cm(-2) s(-1). For ATLAS, an upgrade scenario will imply the complete replacement of its internal tracker, as the existing detector will not provide the required performance due to the cumulated radiation damage and the increase in the detector occupancy. The current baseline layout for the new ATLAS tracker is an all-silicon-based detector, with pixel sensors in the inner layers and silicon micro-strip detectors at intermediate and outer radii. The super-module is an integration concept proposed for the strip region of the future ATLAS tracker, where double-sided stereo silicon micro-strip modules are assembled into a low-mass local support structure. An electrical super-module prototype for eight double-sided strip modules has been constructed. The aim is to exercise the multi-module readout chain and to investigate the noise performance of such a system. In this paper, the main components of the current super-module prototype are described and its electrical performance is presented in detail.
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HAWC Collaboration(Abeysekara, A. U. et al), & Salesa Greus, F. (2023). The High-Altitude Water Cherenkov (HAWC) observatory in Mexico: The primary detector. Nucl. Instrum. Methods Phys. Res. A, 1052, 168253–18pp.
Abstract: The High-Altitude Water Cherenkov (HAWC) observatory is a second-generation continuously operated, wide field-of-view, TeV gamma-ray observatory. The HAWC observatory and its analysis techniques build on experience of the Milagro experiment in using ground-based water Cherenkov detectors for gamma-ray astronomy. HAWC is located on the Sierra Negra volcano in Mexico at an elevation of 4100 meters above sea level. The completed HAWC observatory principal detector (HAWC) consists of 300 closely spaced water Cherenkov detectors, each equipped with four photomultiplier tubes to provide timing and charge information to reconstruct the extensive air shower energy and arrival direction. The HAWC observatory has been optimized to observe transient and steady emission from sources of gamma rays within an energy range from several hundred GeV to several hundred TeV. However, most of the air showers detected are initiated by cosmic rays, allowing studies of cosmic rays also to be performed. This paper describes the characteristics of the HAWC main array and its hardware.
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2020). 3HWC: The Third HAWC Catalog of Very-high-energy Gamma-Ray Sources. Astrophys. J., 905(1), 76–14pp.
Abstract: We present a new catalog of TeV gamma-ray sources using 1523 days of data from the High-Altitude Water Cherenkov (HAWC) Observatory. The catalog represents the most sensitive survey of the northern gamma-ray sky at energies above several TeV, with three times the exposure compared to the previous HAWC catalog, 2HWC. We report 65 sources detected at >= 5 sigma significance, along with the positions and spectral fits for each source. The catalog contains eight sources that have no counterpart in the 2HWC catalog, but are within 1 degrees of previously detected TeV emitters, and 20 sources that are more than 1 degrees away from any previously detected TeV source. Of these 20 new sources, 14 have a potential counterpart in the fourth Fermi Large Area Telescope catalog of gamma-ray sources. We also explore potential associations of 3HWC sources with pulsars in the Australia Telescope National Facility (ATNF) pulsar catalog and supernova remnants in the Galactic supernova remnant catalog.
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HAWC Collaboration(Alfaro, R. et al), & Salesa Greus, F. (2022). Gamma/hadron separation with the HAWC observatory. Nucl. Instrum. Methods Phys. Res. A, 1039, 166984–13pp.
Abstract: The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes atmospheric showers produced by incident gamma rays and cosmic rays with energy from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray sources using ground-based gamma-ray detectors like HAWC is to identify the showers produced by gamma rays or hadrons. The HAWC observatory records roughly 25,000 events per second, with hadrons representing the vast majority (> 99.9%) of these events. The standard gamma/hadron separation technique in HAWC uses a simple rectangular cut involving only two parameters. This work describes the implementation of more sophisticated gamma/hadron separation techniques, via machine learning methods (boosted decision trees and neural networks), and summarizes the resulting improvements in gamma/hadron separation obtained in HAWC.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
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