Doncel, M., Cederwall, B., Martin, S., Quintana, B., Gadea, A., Farnea, E., et al. (2015). Conceptual design of a high resolution Ge array with tracking and imaging capabilities for the DESPEC (FAIR) experiment. J. Instrum., 10, P06010–15pp.
Abstract: We present results of Monte Carlo simulations for the conceptual design of the high-resolution DESPEC Germanium Array Spectrometer (DEGAS) proposed for the Facility for Ion and Antiproton Research (FAIR) under construction at Darmstadt, Germany. The project is carried out in three phases, although only results for the two first phases will be addressed in this work. The first phase will consist of a re-arrangement of the EUROBALL cluster detectors previously used in the RISING campaign at GSI. The second phase is based on coupling AGATA-type triple-cluster detectors with EUROBALL cluster detectors in a compact geometry around the active ion implantation target of DESPEC.
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Andricek, L., Boronat, M., Fuster, J., Garcia, I., Gomis, P., Marinas, C., et al. (2016). Integrated cooling channels in position-sensitive silicon detectors. J. Instrum., 11, P06018–15pp.
Abstract: We present an approach to construct position-sensitive silicon detectors with an integrated cooling circuit. Tests on samples demonstrate that a very modest liquid flow very effectively cool the devices up to a power dissipation of over 10 W/cm(2). The liquid flow is found to have a negligible impact on the mechanical stability. A finite-element simulation predicts the cooling performance to an accuracy of approximately 10%.
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NEXT Collaboration(Navarro, K. E. et al), Carcel, S., Carrion, J. V., Lopez, F., Lopez-March, N., Martin-Albo, J., et al. (2023). A compact dication source for Ba2+ tagging and heavy metal ion sensor development. J. Instrum., 18(7), P07044–19pp.
Abstract: We present a tunable metal ion beam that delivers controllable ion currents in the picoamp range for testing of dry-phase ion sensors. Ion beams are formed by sequential atomic evaporation and single or multiple electron impact ionization, followed by acceleration into a sensing region. Controllability of the ionic charge state is achieved through tuning of electrode potentials that influence the retention time in the ionization region. Barium, lead, and cadmium samples have been used to test the system, with ion currents identified and quantified using a quadrupole mass analyzer. Realization of a clean Ba2+ ion beam within a bench-top system represents an important technical advance toward the development and characterization of barium tagging systems for neutrinoless double beta decay searches in xenon gas. This system also provides a testbed for investigation of novel ion sensing methodologies for environmental assay applications, with dication beams of Pb2+ and Cd2+ also demonstrated for this purpose.
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Nygren, D. R., Jones, B. J. P., Lopez-March, N., Mei, Y., Psihas, F., & Renner, J. (2018). Neutrinoless double beta decay with 82SeF6 and direct ion imaging. J. Instrum., 13, P03015–23pp.
Abstract: We present a new neutrinoless double beta decay concept: the high pressure selenium hexafluoride gas time projection chamber. A promising new detection technique is outlined which combines techniques pioneered in high pressure xenon gas, such as topological discrimination, with the high Q-value afforded by the double beta decay isotope Se-82. The lack of free electrons in SeF6 mandates the use of an ion TPC. The microphysics of ion production and drift, which have many nuances, are explored. Background estimates are presented, suggesting that such a detector may achieve background indices of better than 1 count per ton per year in the region of interest at the 100 kg scale, and still better at the ton-scale.
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NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
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