LHCb Collaboration(Aaij, R. et al), Garcia Martin, L. M., Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., et al. (2019). Measurement of the electron reconstruction efficiency at LHCb. J. Instrum., 14, P11023–20pp.
Abstract: The single electron track-reconstruction efficiency is calibrated using a sample corresponding to 1.3 fb(-1) of pp collision data recorded with the LHCb detector in 2017. This measurement exploits B+ -> J/psi (e(+)e(-))K+ decays, where one of the electrons is fully reconstructed and paired with the kaon, while the other electron is reconstructed using only the information of the vertex detector. Despite this partial reconstruction, kinematic and geometric constraints allow the B meson mass to be reconstructed and the signal to be well separated from backgrounds. This in turn allows the electron reconstruction efficiency to be measured by matching the partial track segment found in the vertex detector to tracks found by LHCb's regular reconstruction algorithms. The agreement between data and simulation is evaluated, and corrections are derived for simulated electrons in bins of kinematics. These correction factors allow LHCb to measure branching fractions involving single electrons with a systematic uncertainty below 1%.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., et al. (2019). Electron and photon performance measurements with the ATLAS detector using the 2015-2017 LHC proton-proton collision data. J. Instrum., 14, P12006–69pp.
Abstract: This paper describes the reconstruction of electrons and photons with the ATLAS detector, employed for measurements and searches exploiting the complete LHC Run 2 dataset. An improved energy clustering algorithm is introduced, and its implications for the measurement and identification of prompt electrons and photons are discussed in detail. Corrections and calibrations that affect performance, including energy calibration, identification and isolation efficiencies, and the measurement of the charge of reconstructed electron candidates are determined using up to 81 fb(-1) of proton-proton collision data collected at root s = 13 TeV between 2015 and 2017.
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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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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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Carrio, F. (2022). The Data Acquisition System for the ATLAS Tile Calorimeter Phase-II Upgrade Demonstrator. IEEE Trans. Nucl. Sci., 69(4), 687–695.
Abstract: The tile calorimeter (TileCal) is the central hadronic calorimeter of the ATLAS experiment at the large hadron collider (LHC). In 2025, the LHC will be upgraded leading to the high luminosity LHC (HL-LHC). The HL-LHC will deliver an instantaneous luminosity up to seven times larger than the LHC nominal luminosity. The ATLAS Phase-II upgrade (2025-2027) will accommodate the subdetectors to the HL-LHC requirements. As part of this upgrade, the majority of the TileCal on-detector and off-detector electronics will be replaced using a new readout strategy, where the on-detector electronics will digitize and transmit digitized detector data to the off-detector electronics at the bunch crossing frequency (40 MHz). In the counting rooms, the off-detector electronics will compute reconstructed trigger objects for the first-level trigger and will store the digitized samples in pipelined buffers until the reception of a trigger acceptance signal. The off-detector electronics will also distribute the LHC clock to the on-detector electronics embedded within the digital data stream. The TileCal Phase-II upgrade project has undertaken an extensive research and development program that includes the development of a Demonstrator module to evaluate the performance of the new clock and readout architecture envisaged for the HL-LHC. The Demonstrator module equipped with the latest version of the on-detector electronics was built and inserted into the ATLAS experiment. The Demonstrator module is operated and read out using a Tile PreProcessor (TilePPr) Demonstrator which enables backward compatibility with the present ATLAS Trigger and Data AcQuisition (TDAQ), and the timing, trigger, and command (TTC) systems. This article describes in detail the main hardware and firmware components of the clock distribution and data acquisition systems for the Demonstrator module, focusing on the TilePPr Demonstrator.
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