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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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Contreras, T., Martins, A., Stanford, C., Escobar, C. O., Guenette, R., Stancari, M., et al. (2023). A method to characterize metalenses for light collection applications. J. Instrum., 18(9), T09004–11pp.
Abstract: Metalenses and metasurfaces are promising emerging technologies that could improve light collection in light collection detectors, concentrating light on small area photodetectors such as silicon photomultipliers. Here we present a detailed method to characterize metalenses to assess their efficiency at concentrating monochromatic light coming from a wide range of incidence angles, not taking into account their imaging quality.
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