Alioli, S., Fuster, J., Garzelli, M. V., Gavardi, A., Irles, A., Melini, D., et al. (2022). Phenomenology of t(t)over-barj plus X production at the LHC. J. High Energy Phys., 05(5), 146–63pp.
Abstract: We present phenomenological results for t (t) over barj + X production at the Large Hadron Collider, of interest for designing forthcoming experimental analyses of this process. We focus on those cases where the t (t) over barj + X process is considered as a signal. We discuss present theoretical uncertainties and the dependence on relevant input parameters entering the computation. For the R. distribution, which depends on the invariant mass of the t (t) over barj-system, we present reference predictions in the on-shell, (MS) over bar and MSR top-quark mass renormalization schemes, applying the latter scheme to this process for the first time. Our conclusions are particularly interesting for those analyses aiming at extracting the topquark mass from cross-section measurements.
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Aparisi, J., Fuster, J., Irles, A., Rodrigo, G., Vos, M., Yamamoto, H., et al. (2022). m(b) at m(H): The Running Bottom Quark Mass and the Higgs Boson. Phys. Rev. Lett., 128(12), 122001–7pp.
Abstract: We present a new measurement of the bottom quark mass in the MS scheme at the renormalization scale of the Higgs boson mass from measurements of Higgs boson decay rates at the LHC: -0.31 GeV. The measurement has a negligible theory uncertainty and excellent prospects to improve at the HL-LHC and a future Higgs factory. Confronting this result and mb(mb) from low-energy measurements and mb(mZ) from Z-pole data, with the prediction of the scale evolution of the renormalization group equations, we find strong evidence for the “running” of the bottom quark mass.
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CALICE Collaboration(Lai, S. et al), & Irles, A. (2024). Software compensation for highly granular calorimeters using machine learning. J. Instrum., 19(4), P04037–28pp.
Abstract: A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
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CALICE Collaboration(White, A. et al), & Irles, A. (2023). Design, construction and commissioning of a technological prototype of a highly granular SiPM-on-tile scintillator-steel hadronic calorimeter. J. Instrum., 18(11), P11018–39pp.
Abstract: The CALICE collaboration is developing highly granular electromagnetic and hadronic calorimeters for detectors at future energy frontier electron-positron colliders. After successful tests of a physics prototype, a technological prototype of the Analog Hadron Calorimeter has been built, based on a design and construction techniques scalable to a collider detector. The prototype consists of a steel absorber structure and active layers of small scintillator tiles that are individually read out by directly coupled SiPMs. Each layer has an active area of 72 x 72 cm2 and a tile size of 3 x 3 cm2. With 38 active layers, the prototype has nearly 22, 000 readout channels, and its total thickness amounts to 4.4 nuclear interaction lengths. The dedicated readout electronics provide time stamping of each hit with an expected resolution of about 1 ns. The prototype was constructed in 2017 and commissioned in beam tests at DESY. It recorded muons, hadron showers and electron showers at different energies in test beams at CERN in 2018. In this paper, the design of the prototype, its construction and commissioning are described. The methods used to calibrate the detector are detailed, and the performance achieved in terms of uniformity and stability is presented.
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CMS and CALICE Collaborations(Acar, B. et al), & Irles, A. (2023). Performance of the CMS High Granularity Calorimeter prototype to charged pion beams of 20-300 GeV/c. J. Instrum., 18(8), P08014–32pp.
Abstract: The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based on silicon sensors interspersed between lead and copper (or copper tungsten) absorbers. The hadronic section uses layers of stainless steel as an absorbing medium and silicon sensors as an active medium in the regions of high radiation exposure, and scintillator tiles directly read out by silicon photomultipliers in the remaining regions. As part of the development of the detector and its readout electronic components, a section of a silicon-based HGCAL prototype detector along with a section of the CALICE AHCAL prototype was exposed to muons, electrons and charged pions in beam test experiments at the H2 beamline at the CERN SPS in October 2018. The AHCAL uses the same technology as foreseen for the HGCAL but with much finer longitudinal segmentation. The performance of the calorimeters in terms of energy response and resolution, longitudinal and transverse shower profiles is studied using negatively charged pions, and is compared to GEANT4 predictions. This is the first report summarizing results of hadronic showers measured by the HGCAL prototype using beam test data.
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