Perez-Calatayud, J., Ballester, F., Tedgren, C., DeWerd, L. A., Papagiannis, P., Rivard, M. J., et al. (2022). GEC-ESTRO ACROP recommendations on calibration and traceability of HE HDR-PDR photon-emitting brachytherapy sources at the hospital level. Radiother. Oncol., 176, 108–117.
Abstract: The vast majority of radiotherapy departments in Europe using brachytherapy (BT) perform temporary implants of high-or pulsed-dose rate (HDR-PDR) sources with photon energies higher than 50 keV. Such techniques are successfully applied to diverse pathologies and clinical scenarios. These recommen-dations are the result of Working Package 21 (WP-21) initiated within the BRAchytherapy PHYsics Quality Assurance System (BRAPHYQS) GEC-ESTRO working group with a focus on HDR-PDR source cal-ibration. They provide guidance on the calibration of such sources, including practical aspects and issues not specifically accounted for in well-accepted societal recommendations, complementing the BRAPHYQS WP-18 Report dedicated to low energy BT photon emitting sources (seeds). The aim of this report is to provide a European-wide standard in HDR-PDR BT source calibration at the hospital level to maintain high quality patient treatments.
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ATLAS Collaboration(Aaboud, M. et al), Alvarez Piqueras, D., Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., et al. (2019). Electron and photon energy calibration with the ATLAS detector using 2015-2016 LHC proton-proton collision data. J. Instrum., 14, P03017–60pp.
Abstract: This paper presents the electron and photon energy calibration obtained with the ATLAS detector using about 36 fb(-1) of LHC proton-proton collision data recorded at root s = 13 TeV in 2015 and 2016. The different calibration steps applied to the data and the optimization of the reconstruction of electron and photon energies are discussed. The absolute energy scale is set using a large sample of Z boson decays into electron-positron pairs. The systematic uncertainty in the energy scale calibration varies between 0.03% to 0.2% in most of the detector acceptance for electrons with transverse momentum close to 45 GeV. For electrons with transverse momentum of 10 GeV the typical uncertainty is 0.3% to 0.8% and it varies between 0.25% and 1% for photons with transverse momentum around 60 GeV. Validations of the energy calibration with J/psi -> e(+)e(-) decays and radiative Z boson decays are also presented.
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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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An, L., Auffray, E., Betti, F., Dall'Omo, F., Gascon, D., Golutvin, A., et al. (2023). Performance of a spaghetti calorimeter prototype with tungsten absorber and garnet crystal fibres. Nucl. Instrum. Methods Phys. Res. A, 1045, 167629–7pp.
Abstract: A spaghetti calorimeter (SPACAL) prototype with scintillating crystal fibres was assembled and tested with electron beams of energy from 1 to 5 GeV. The prototype comprised radiation-hard Cerium-doped Gd3Al2Ga3O12 (GAGG:Ce) and Y3Al5O12 (YAG:Ce) embedded in a pure tungsten absorber. The energy resolution root was studied as a function of the incidence angle of the beam and found to be of the order of 10%/ E a 1%, in line with the LHCb Shashlik technology. The time resolution was measured with metal channel dynode photomultipliers placed in contact with the fibres or coupled via a light guide, additionally testing an optical tape to glue the components. Time resolution of a few tens of picosecond was achieved for all the energies reaching down to (18.5 +/- 0.2) ps at 5 GeV.
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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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