|
|
ANTARES and IceCube Collaborations(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2020). ANTARES and IceCube Combined Search for Neutrino Point-like and Extended Sources in the Southern Sky. Astrophys. J., 892(2), 92–12pp.
Abstract: A search for point-like and extended sources of cosmic neutrinos using data collected by the ANTARES and IceCube neutrino telescopes is presented. The data set consists of all the track-like and shower-like events pointing in the direction of the Southern Sky included in the nine-year ANTARES point-source analysis, combined with the throughgoing track-like events used in the seven-year IceCube point-source search. The advantageous field of view of ANTARES and the large size of IceCube are exploited to improve the sensitivity in the Southern Sky by a factor of similar to 2 compared to both individual analyses. In this work, the Southern Sky is scanned for possible excesses of spatial clustering, and the positions of preselected candidate sources are investigated. In addition, special focus is given to the region around the Galactic Center, whereby a dedicated search at the location of SgrA* is performed, and to the location of the supernova remnant RXJ 1713.7-3946. No significant evidence for cosmic neutrino sources is found, and upper limits on the flux from the various searches are presented.
|
|
|
|
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.
|
|
|
|
ANTARES and IceCube Collaborations(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2020). Combined search for neutrinos from dark matter self-annihilation in the Galactic Center with ANTARES and IceCube. Phys. Rev. D, 102(8), 082002–13pp.
Abstract: We present the results of the first combined dark matter search targeting the Galactic Center using the ANTARES and IceCube neutrino telescopes. For dark matter particles with masses from 50 to 1000 GeV, the sensitivities on the self-annihilation cross section set by ANTARES and IceCube are comparable, making this mass range particularly interesting for a joint analysis. Dark matter self-annihilation through the tau(+)tau(-) , mu(+)mu(-) , b (b) over bar, and W+W- channels is considered for both the Navarro-Frenk-White and Burkert halo profiles. In the combination of 2101.6 days of ANTARES data and 1007 days of IceCube data, no excess over the expected background is observed. Limits on the thermally averaged dark matter annihilation cross section <sigma(A)upsilon > are set. These limits present an improvement of up to a factor of 2 in the studied dark matter mass range with respect to the individual limits published by both collaborations. When considering dark matter particles with a mass of 200 GeV annihilating through the tau(+)tau(-)channel, the value obtained for the limit is 7.44 x 10(-24) cm(3) s(-1 )for the Navarro-Frenk-White halo profile. For the purpose of this joint analysis, the model parameters and the likelihood are unified, providing a benchmark for forthcoming dark matter searches performed by neutrino telescopes.
|
|
|
|
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.
|
|
|
|
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.
|
|
