The Gaseous Detectors R&D Group at LIP studies the performance of gas detectors in the challenging range of low energy (below a few hundred keV), and more recently also in the high energy range (of a few MeV). Its main research areas at the moment are the study of the drift parameters of electrons and ions in the gases used as detector’s fillings (noble gases and their mixtures with moleccular ones), with the purpose of finding the more suitable one for each application, namely for large volume detectors or low-energy range ones. Custom made Monte Carlo simulation software specifically developed for the study of these parameters is used to compare and explain the experimental results obtained with the equipment that exists in the lab. This equipment was mainly developed by the group and includes gas detector prototypes and also experimental systems to measure the required drift parameters, namely ion and electron mobility and also electron diffusion.
The group knowledge in this area is the basis of its involvement in the NEXT collaboration, that uses a high pressure xenon TPC to search for neutrinoless double beta decay, and also in the RD51collaboration at CERN, that aims at developing new techniques in gaseous detectors, for which the knowledge of ion and electron drift parameters is very important.
Photos
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Sensitivity of a tonne-scale NEXT detector for neutrinoless double-beta decay searches
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Author(s): NEXT Collaboration (98 authors)
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Submission: 2021-08-30, Acceptance: 2021-08-30, Publication: 2021-08-30
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Reference: J. High Energy Phys. 8 (2021) 164
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Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution
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Author(s): NEXT Collaboration (103 authors)
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Submission: 2021-07-21, Acceptance: 2021-07-21, Publication: 2021-07-21
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Reference: J. High Energy Phys. 7 (2021) 146
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Sensitivity of the NEXT experiment to Xe-124 double electron capture
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Author(s): NEXT Collaboration (91 authors)
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Submission: 2021-02-24, Acceptance: 2021-02-24, Publication: 2021-02-24
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Reference: J. High Energy Phys. 2 (2021) 203
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Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
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Author(s): NEXT Collaboration (90 authors)
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Submission: 2021-01-28, Acceptance: 2021-01-28, Publication: 2021-01-28
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Reference: J. High Energy Phys. 1 (2021) 189