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Poley, L. et al, & Lacasta, C. (2017). Investigations into the impact of locally modified sensor architectures on the detection efficiency of silicon micro-strip sensors. J. Instrum., 12, P07006–17pp.
Abstract: The High Luminosity Upgrade of the LHC will require the replacement of the Inner Detector of ATLAS with the Inner Tracker (ITk) in order to cope with higher radiation levels and higher track densities. Prototype silicon strip detector modules are currently developed and their performance is studied in both particle test beams and X-ray beams. In previous test beam measurements of prototype modules, the response of silicon sensors has been studied in detailed scans across individual sensor strips. These scans found instances of sensor strips collecting charge across areas on the sensor deviating from the geometrical width of a sensor strip. The variations have been linked to local features of the sensor architecture. This paper presents results of detailed sensor measurements in both X-ray and particle beams investigating the impact of sensor features (metal pads and p-stops) on the sensor strip response.
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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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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Identification of charm jets at LHCb. J. Instrum., 17(2), P02028–23pp.
Abstract: The identification of charm jets is achieved at LHCb for data collected in 2015-2018 using a method based on the properties of displaced vertices reconstructed and matched with jets. The performance of this method is determined using a dijet calibration dataset recorded by the LHCb detector and selected such that the jets are unbiased in quantities used in the tagging algorithm. The charm-tagging efficiency is reported as a function of the transverse momentum of the jet. The measured efficiencies are compared to those obtained from simulation and found to be in good agreement.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fernandez Martinez, P., et al. (2016). Performance of b-jet identification in the ATLAS experiment. J. Instrum., 11, P04008–126pp.
Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.
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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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