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ATLAS Collaboration(Aad, G. et al), Aikot, A., Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Bouchhar, N., et al. (2024). Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data. J. Instrum., 19(2), P02009–58pp.
Abstract: This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb-1 of LHC proton -proton collision data recorded at -Js = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z -boson decays into electron -positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z -boson decays, 0.4% at ET – 10 GeV, and 0.3% at ET – 1 TeV; for photons at ET <^>' 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using .11tfr -, ee and radiative Z -boson decays.
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Ortiz Arciniega, J. L., Carrio, F., & Valero, A. (2019). FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions. J. Instrum., 14, P09002–13pp.
Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.
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NEXT Collaboration(Alvarez, V. et al), Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., Gil, A., et al. (2013). Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment. J. Instrum., 8, P04002–25pp.
Abstract: NEXT-DEMO is a large-scale prototype of the NEXT-100 detector, an electroluminescent time projection chamber that will search for the neutrinoless double beta decay of Xe-136 using 100-150 kg of enriched xenon gas. NEXT-DEMO was built to prove the expected performance of NEXT-100, namely, energy resolution better than 1% FWHM at 2.5MeV and event topological reconstruction. In this paper we describe the prototype and its initial results. A resolution of 1.75% FWHM at 511 keV (which extrapolates to 0.8% FWHM at 2.5 MeV) was obtained at 10 bar pressure using a gamma-ray calibration source. Also, a basic study of the event topology along the longitudinal coordinate is presented, proving that it is possible to identify the distinct dE/dx of electron tracks in high-pressure xenon using an electroluminescence TPC.
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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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LHCb Collaboration(Aaij, R. et al), Martinez-Vidal, F., Oyanguren, A., Ruiz Valls, P., & Sanchez Mayordomo, C. (2015). Measurement of the track reconstruction efficiency at LHCb. J. Instrum., 10, P02007–23pp.
Abstract: The determination of track reconstruction efficiencies at LHCb using J/psi -> mu(+)mu(-) decays is presented. Efficiencies above 95% are found for the data taking periods in 2010, 2011, and 2012. The ratio of the track reconstruction efficiency of muons in data and simulation is compatible with unity and measured with an uncertainty of 0.8% for data taking in 2010, and at a precision of 0.4% for data taking in 2011 and 2012. For hadrons an additional 1.4% uncertainty due to material interactions is assumed. This result is crucial for accurate cross section and branching fraction measurements in LHCb.
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