Deep learning reconstruction in ANTARES - García-Méndez, J.; Geißelbrecht, Nicole; Eberl, Thomas; Ardid, M.; Ardid, S. (Journal of Instrumentation, 2021)
ANTARES is currently the largest undersea neutrino telescope, located in the Mediterranean Sea and taking data since 2007. It consists of a 3D array of photo sensors, instrumenting about 10Mt of seawater to detect Cherenkov light induced by secondary particles from neutrino interactions. The event reconstruction and background discrimination is challenging and machine-learning techniques are explored to improve the performance. In this contribution, two case studies using deep convolutional neural networks are presented. In the first one, this approach is used to improve the direction reconstruction of low-energy single-line events, for which the reconstruction of the azimuth angle of the incoming neutrino is particularly difficult. We observe a promising improvement in resolution over classical reconstruction techniques and expect to at least double our sensitivity in the low-energy range, important for dark matter searches. The second study employs deep learning to reconstruct the visible energy of neutrino interactions of all flavors and for the multi-line setup of the full detector.
CRIS-Team Scopus Importer:2021-10-08
Keywords: Analysis and statistical methods; Data processing methods; Neutrino detectors; Pattern recognition; cluster finding; calibration and fitting methods

Deep-sea deployment of the KM3NeT neutrino telescope detection units by self-unrolling - Aiello, S.; Domi, Alba; Albert, A.; Garre, S. Alves; Aly, Z.; Ameli, F.; Anassontzis, E.; Andre, M.; Androulakis, G.; Anghinolfi, M.; Anguita, M.; Anton, Gisela; et al. (Journal of Instrumentation, 2020)
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.
CRIS-Team WoS Importer:2020-12-18

Event reconstruction for KM3NeT/ORCA using convolutional neural networks - Aiello, S.; Albert, A.; Garre, S. Alves; Aly, Z.; Ameli, F.; Andre, M.; Androulakis, G.; Anghinolfi, M.; Anguita, M.; Anton, Gisela; Ardid, M.; Aublin, J.; et al. (Journal of Instrumentation, 2020)
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.
CRIS-Team WoS Importer:2020-10-30

Sensitivity to light sterile neutrino mixing parameters with KM3NeT/ORCA - Aiello, S.; Albert, A.; Alshamsi, M.; Garre, S. Alves; Aly, Z.; Ambrosone, Antonio; Ameli, F.; Andre, M.; Androulakis, G.; Anghinolfi, M.; Anguita, M.; Anton, Gisela; et al. (Journal of High Energy Physics, 2021)
KM3NeT/ORCA is a next-generation neutrino telescope optimised for atmospheric neutrino oscillations studies. In this paper, the sensitivity of ORCA to the presence of a light sterile neutrino in a 3+1 model is presented. After three years of data taking, ORCA will be able to probe the active-sterile mixing angles theta(14), theta(24), theta(34) and the effective angle theta(mu e), over a broad range of mass squared difference Delta m(41)(2) similar to [10(-5), 10] eV(2), allowing to test the eV-mass sterile neutrino hypothesis as the origin of short baseline anomalies, as well as probing the hypothesis of a very light sterile neutrino, not yet constrained by cosmology. ORCA will be able to explore a relevant fraction of the parameter space not yet reached by present measurements.
CRIS-Team WoS Importer:2021-11-12

The KM3NeT potential for the next core-collapse supernova observation with neutrinos: KM3NeT Collaboration - Aiello, S.; Domi, Alba; Albert, A.; Garre, S. Alves; Aly, Z.; Ambrosone, A.; Ameli, F.; Andre, M.; Androulakis, G.; Anghinolfi, M.; Anguita, M.; Anton, Gisela; et al. (European Physical Journal C: Particles and Fields, 2021)
The KM3NeT research infrastructure is under construction in the Mediterranean Sea. It consists of two water Cherenkov neutrino detectors, ARCA and ORCA, aimed at neutrino astrophysics and oscillation research, respectively. Instrumenting a large volume of sea water with ∼ 6200 optical modules comprising a total of ∼ 200 , 000 photomultiplier tubes, KM3NeT will achieve sensitivity to ∼10MeV neutrinos from Galactic and near-Galactic core-collapse supernovae through the observation of coincident hits in photomultipliers above the background. In this paper, the sensitivity of KM3NeT to a supernova explosion is estimated from detailed analyses of background data from the first KM3NeT detection units and simulations of the neutrino signal. The KM3NeT observational horizon (for a 5σ discovery) covers essentially the Milky-Way and for the most optimistic model, extends to the Small Magellanic Cloud (∼60kpc). Detailed studies of the time profile of the neutrino signal allow assessment of the KM3NeT capability to determine the arrival time of the neutrino burst with a few milliseconds precision for sources up to 5–8 kpc away, and detecting the peculiar signature of the standing accretion shock instability if the core-collapse supernova explosion happens closer than 3–5 kpc, depending on the progenitor mass. KM3NeT’s capability to measure the neutrino flux spectral parameters is also presented.
CRIS-Team Scopus Importer:2021-06-11