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KM3NeT Collaboration(Aiello, S. et al), Barrios-Marti, J., Calvo, D., Coleiro, A., Colomer, M., Gozzini, S. R., et al. (2018). Characterisation of the Hamamatsu photomultipliers for the KM3NeT Neutrino Telescope. J. Instrum., 13, P05035–17pp.
Abstract: The Hamamatsu R12199-023-inch photomultiplier tube is the photodetector chosen for the first phase of the KM3NeT neutrino telescope. About 7000 photomultipliers have been characterised for dark count rate, timing spread and spurious pulses. The quantum efficiency, the gain and the peak-to-valley ratio have also been measured for a sub-sample in order to determine parameter values needed as input to numerical simulations of the detector.
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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), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Deep-sea deployment of the KM3NeT neutrino telescope detection units by self-unrolling. J. Instrum., 15(11), P11027–18pp.
Abstract: KM3NeT is a research infrastructure being installed in the deep Mediterranean Sea. It will house a neutrino telescope comprising hundreds of networked moorings – detection units or strings – equipped with optical instrumentation to detect the Cherenkov radiation generated by charged particles from neutrino-induced collisions in its vicinity. In comparison to moorings typically used for oceanography, several key features of the KM3NeT string are different: the instrumentation is contained in transparent and thus unprotected glass spheres; two thin Dyneema (R) ropes are used as strength members; and a thin delicate backbone tube with fibre-optics and copper wires for data and power transmission, respectively, runs along the full length of the mooring. Also, compared to other neutrino telescopes such as ANTARES in the Mediterranean Sea and GVD in Lake Baikal, the KM3NeT strings are more slender to minimise the amount of material used for support of the optical sensors. Moreover, the rate of deploying a large number of strings in a period of a few years is unprecedented. For all these reasons, for the installation of the KM3NeT strings, a custom-made, fast deployment method was designed. Despite the length of several hundreds of metres, the slim design of the string allows it to be compacted into a small, re-usable spherical launching vehicle instead of deploying the mooring weight down from a surface vessel. After being lowered to the seafloor, the string unfurls to its full length with the buoyant launching vehicle rolling along the two ropes. The design of the vehicle, the loading with a string, and its underwater self-unrolling are detailed in this paper.
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ANTARES Collaboration(Albert, A. et al), Barrios-Marti, J., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., Tönnis, C., et al. (2017). Search for dark matter annihilation in the earth using the ANTARES neutrino telescope. Phys. Dark Universe, 16, 41–48.
Abstract: A search for a neutrino signal from WIMP pair annihilations in the centre of the Earth has been performed with the data collected with the ANTARES neutrino telescope from 2007 to 2012. The event selection criteria have been developed and tuned to maximise the sensitivity of the experiment to such a neutrino signal. No significant excess of neutrinos over the expected background has been observed. Upper limits at 90% C.L. on the WIMP annihilation rate in the Earth and the spin independent scattering cross-section of WIMPs to nucleons sigma(SI)(p) were calculated for WIMP pair annihilations into either iota(+) iota(-), W+W-, b (b) over bar or the non-SUSY v mu(v) over bar as a function of the WIMP mass (between 25 GeV/c(2) and 1000 GeV/c(2)) and as a function of the thermally averaged annihilation cross section times velocity <sigma A(v)>(Earth) of the WIMPs in the centre of the Earth. For masses of the WIMP close to the mass of iron nuclei (50 GeV/c(2)), the obtained limits on sigma(SI)(p) are more stringent than those obtained by other indirect searches.
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KM3NeT Collaboration(Adrian-Martinez, S. et al), Barrios-Marti, J., Calvo Diaz-Aldagalan, D., Hernandez-Rey, J. J., Illuminati, G., Lotze, M., et al. (2016). Letter of intent for KM3NeT 2.0. J. Phys. G, 43(8), 084001–130pp.
Abstract: The main objectives of the KM3NeT Collaboration are (i) the discovery and subsequent observation of high-energy neutrino sources in the Universe and (ii) the determination of the mass hierarchy of neutrinos. These objectives are strongly motivated by two recent important discoveries, namely: (1) the high-energy astrophysical neutrino signal reported by IceCube and (2) the sizable contribution of electron neutrinos to the third neutrino mass eigenstate as reported by Daya Bay, Reno and others. To meet these objectives, the KM3NeT Collaboration plans to build a new Research Infrastructure consisting of a network of deep-sea neutrino telescopes in the Mediterranean Sea. A phased and distributed implementation is pursued which maximises the access to regional funds, the availability of human resources and the synergistic opportunities for the Earth and sea sciences community. Three suitable deep-sea sites are selected, namely off-shore Toulon (France), Capo Passero (Sicily, Italy) and Pylos (Peloponnese, Greece). The infrastructure will consist of three so-called building blocks. A building block comprises 115 strings, each string comprises 18 optical modules and each optical module comprises 31 photo-multiplier tubes. Each building block thus constitutes a three-dimensional array of photo sensors that can be used to detect the Cherenkov light produced by relativistic particles emerging from neutrino interactions. Two building blocks will be sparsely configured to fully explore the IceCube signal with similar instrumented volume, different methodology, improved resolution and complementary field of view, including the galactic plane. One building block will be densely configured to precisely measure atmospheric neutrino oscillations.
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