Perez-Perez, J., Amare, J. C., Bandac, I. C., Bayo, A., Borjabad-Sanchez, S., Calvo-Mozota, J. M., et al. (2022). Radon Mitigation Applications at the Laboratorio Subterráneo de Canfranc (LSC). Universe, 8(2), 112–20pp.
Abstract: The Laboratorio Subterraneo de Canfranc (LSC) is the Spanish national hub for low radioactivity techniques and the associated scientific and technological applications. The concentration of the airborne radon is a major component of the radioactive budget in the neighborhood of the detectors. The LSC hosts a Radon Abatement System, which delivers a radon suppressed air with 1.1 & PLUSMN;0.2 mBq/m(3) of Rn-222. The radon content in the air is continuously monitored with an Electrostatic Radon Monitor. Measurements with the double beta decay demonstrators NEXT-NEW and CROSS and the gamma HPGe detectors show the important reduction of the radioactive background due to the purified air in the vicinity of the detectors. We also discuss the use of this facility in the LSC current program which includes NEXT-100, low background biology experiments and radiopure copper electroformation equipment placed in the radon-free clean room.
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Garcia, A. R., Martinez, T., Cano-Ott, D., Castilla, J., Guerrero, C., Marin, J., et al. (2012). MONSTER: a time of flight spectrometer for beta-delayed neutron emission measurements. J. Instrum., 7, C05012–12pp.
Abstract: The knowledge of the beta-decay properties of nuclei contributes decisively to our understanding of nuclear phenomena: the beta-delayed neutron emission of neutron rich nuclei plays an important role in the nucleosynthesis r-process and constitutes a probe for nuclear structure of very neutron rich nuclei providing information about the high energy part of the full beta strength (S-beta) function. In addition, beta-delayed neutrons are essential for the control and safety of nuclear reactors. In order to determine the neutron energy spectra and emission probabilities from neutron precursors a MOdular Neutron time-of-flight SpectromeTER (MONSTER) has been proposed for the DESPEC experiment at the future FAIR facility. The design of MONSTER and status of its construction are reported in this work.
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Calvo, D., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2016). A method to stabilise the performance of negatively fed KM3NeT photomultipliers. J. Instrum., 11, P12014–12pp.
Abstract: The KM3NeT research infrastructure, currently under construction in the Mediterranean Sea, will host neutrino telescopes for the identification of neutrino sources in the Universe and for studies of the neutrino mass hierarchy. These telescopes will house hundreds of thousands of photomultiplier tubes that will have to be operated in a stable and reliable fashion. In this context, the stability of the dark counts has been investigated for photomultiplier tubes with negative high voltage on the photocathode and held in insulating support structures made of 3D printed nylon material. Small gaps between the rigid support structure and the photomultiplier tubes in the presence of electric fields can lead to discharges that produce dark count rates that are highly variable. A solution was found by applying the same insulating varnish as used for the high voltage bases directly to the outside of the photomultiplier tubes. This transparent conformal coating provides a convenient and inexpensive method of insulation.
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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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KM3NeT Collaboration(Aiello, S. et al), Calvo, D., Coleiro, A., Colomer, M., Gozzini, S. R., Hernandez-Rey, J. J., et al. (2020). The Control Unit of the KM3NeT Data Acquisition System. Comput. Phys. Commun., 256, 107433–16pp.
Abstract: The KM3NeT Collaboration runs a multi-site neutrino observatory in the Mediterranean Sea. Water Cherenkov particle detectors, deep in the sea and far off the coasts of France and Italy, are already taking data while incremental construction progresses. Data Acquisition Control software is operating off-shore detectors as well as testing and qualification stations for their components. The software, named Control Unit, is highly modular. It can undergo upgrades and reconfiguration with the acquisition running. Interplay with the central database of the Collaboration is obtained in a way that allows for data taking even if Internet links fail. In order to simplify the management of computing resources in the long term, and to cope with possible hardware failures of one or more computers, the KM3NeT Control Unit software features a custom dynamic resource provisioning and failover technology, which is especially important for ensuring continuity in case of rare transient events in multi-messenger astronomy. The software architecture relies on ubiquitous tools and broadly adopted technologies and has been successfully tested on several operating systems.
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