KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Hernandez-Rey, J. J., et al. (2021). The KM3NeT potential for the next core-collapse supernova observation with neutrinos. Eur. Phys. J. C, 81(5), 445–19pp.
Abstract: 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 similar to 6200 optical modules comprising a total of similar to 200,000 photomultiplier tubes, KM3NeT will achieve sensitivity to similar to 10 MeV 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 5 sigma discovery) covers essentially the Milky-Way and for the most optimistic model, extends to the Small Magellanic Cloud (similar to 60 kpc). 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.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Hernandez-Rey, J. J., et al. (2021). Sensitivity to light sterile neutrino mixing parameters with KM3NeT/ORCA. J. High Energy Phys., 10(10), 180–26pp.
Abstract: 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.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). gSeaGen: The KM3NeT GENIE-based code for neutrino telescopes. Comput. Phys. Commun., 256, 107477–15pp.
Abstract: The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able to generate events induced by all neutrino flavours, considering topological differences between tracktype and shower-like events. Neutrino interactions are simulated taking into account the density and the composition of the media surrounding the detector. The main features of gSeaGen are presented together with some examples of its application within the KM3NeT project. Program summary Program Title: gSeaGen CPC Library link to program files: http://dx.doi.org/10.17632/ymgxvy2br4.1 Licensing provisions: GPLv3 Programming language: C++ External routines/libraries: GENIE [1] and its external dependencies. Linkable to MUSIC [2] and PROPOSAL [3]. Nature of problem: Development of a code to generate detectable events in neutrino telescopes, using modern and maintained neutrino interaction simulation libraries which include the state-of-the-art physics models. The default application is the simulation of neutrino interactions within KM3NeT [4]. Solution method: Neutrino interactions are simulated using GENIE, a modern framework for Monte Carlo event generators. The GENIE framework, used by nearly all modern neutrino experiments, is considered as a reference code within the neutrino community. Additional comments including restrictions and unusual features: The code was tested with GENIE version 2.12.10 and it is linkable with release series 3. Presently valid up to 5 TeV. This limitation is not intrinsic to the code but due to the present GENIE valid energy range. References: [1] C. Andreopoulos at al., Nucl. Instrum. Meth. A614 (2010) 87. [2] P. Antonioli et al., Astropart. Phys. 7 (1997) 357. [3] J. H. Koehne et al., Comput. Phys. Commun. 184 (2013) 2070. [4] S. Adrian-Martinez et al., J. Phys. G: Nucl. Part. Phys. 43 (2016) 084001.
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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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