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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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., et al. (2022). Operation and performance of the ATLAS semiconductor tracker in LHC Run 2. J. Instrum., 17(1), P01013–56pp.
Abstract: The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules. During Run 2 (2015-2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb(-1) to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector. Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2. It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%. Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules. '
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Boronat, M., Marinas, C., Frey, A., Garcia, I., Schwenker, B., Vos, M., et al. (2015). Physical Limitations to the Spatial Resolution of Solid-State Detectors. IEEE Trans. Nucl. Sci., 62(1), 381–386.
Abstract: In this paper we explore the effect of delta-ray emission and fluctuations in the signal deposition on the detection of charged particles in silicon-based detectors. We show that these two effects ultimately limit the resolution that can be achieved by interpolation of the signal in finely segmented position-sensitive solid-state devices.
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ATLAS Collaboration(Aad, G. et al), Amoros, G., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Escobar, C., et al. (2010). Charged-particle multiplicities in pp interactions at root s=900 GeV measured with the ATLAS detector at the LHC. Phys. Lett. B, 688(1), 21–42.
Abstract: The first measurements from proton-proton collisions recorded with the ATLAS detector at the LHC are presented. Data were collected in December 2009 using a minimum-bias trigger during collisions at a centre-of-mass energy of 900 GeV. The charged-particle multiplicity, its dependence on transverse momentum and pseudorapidity, and the relationship between mean transverse momentum and charged-particle multiplicity are measured for events with at least one charged particle in the kinematic range vertical bar eta vertical bar < 2.5 and p(T) > 500 MeV. The measurements are compared to Monte Carlo models of proton-proton collisions and to results from other experiments at the same centre-of-mass energy. The charged-particle multiplicity per event and unit of pseudorapidity eta = 0 is measured to be 1.333 +/- 0.003(stat.) +/- 0.040(syst.), which is 5-15% higher than the Monte Carlo models predict.
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