scholarly journals KM3NeT sensitivity to low energy astrophysical neutrinos

2021 ◽  
Vol 16 (12) ◽  
pp. C12003
Author(s):  
G. de Wasseige
Keyword(s):  
Data Science ◽  
Environmental Noise ◽  
High Energy ◽  
Low Energy ◽  
Mass Hierarchy ◽  
Neutrino Mass Hierarchy ◽  
Neutrino Interactions ◽  
Energy Neutrino ◽  
Low Energies ◽  
Astrophysical Neutrinos

Abstract KM3NeT, a new generation of neutrino telescope, is currently being deployed in the Mediterranean Sea. While its two sites, ORCA and ARCA, were respectively designed for the determination of neutrino mass hierarchy and high-energy neutrino astronomy, this contribution presents a study of the detection potential of KM3NeT in the MeV-GeV energy range. At these low energies, the data rate is dominated by low-energy atmospheric muons and environmental noise due to bioluminescence and K-40 decay. The goal of this study is to characterize the environmental noise in order to optimize the selection of low-energy neutrino interactions and increase the sensitivity of KM3NeT to transient astrophysical phenomena, such as close-by core-collapse supernovae, solar flares, and extragalactic transients. In this contribution, we will study how using data science tools might improve the sensitivity of KM3NeT in these low-energy neutrino searches. We will first introduce the data sets and the different variables used to characterize KM3NeT’s response to the environmental noise. We will then compare the efficiency of various tools in identifying different components in the environmental noise and in disentangling low-energy neutrino interactions from the background events. We will conclude with the implication of low-energy neutrinos for future astrophysical transient searches.

scholarly journals Multi-detector approach to enhance the sensitivity of neutrino telescopes to low-energy astrophysical sources

2021 ◽  
Vol 16 (12) ◽  
pp. C12012
Author(s):  
G. de Wasseige
Keyword(s):  
Energy Range ◽  
Neutrino Flux ◽  
High Energy ◽  
Low Energy ◽  
High Energy Neutrino ◽  
Neutrino Telescopes ◽  
Energy Neutrino ◽  
Astrophysical Neutrinos ◽  
Transient Sources ◽  
The Moment

Abstract While large neutrino telescopes have so far mainly focused on the detection of TeV-PeV astrophysical neutrinos, several efforts are ongoing to extend the sensitivity down to the GeV level for transient sources. Only a handful of neutrino searches have been carried out at the moment leaving the signature of astrophysical transients poorly known in this energy range. In this contribution, we discuss the motivations for high-energy neutrino telescopes to explore the GeV energy range and summarize the current limitations of detectors, such as IceCube and KM3NeT. We then present and compare different approaches for multi-detector analyses that may enhance the sensitivity to a transient GeV neutrino flux.

scholarly journals Status and first results from the ARCA and ORCA lines of the KM3NeT experiment

2019 ◽  
Vol 209 ◽  
pp. 01006
Author(s):  
Paolo Fermani ◽  
Irene Di Palma
Keyword(s):  
High Energy ◽  
Mass Hierarchy ◽  
Atmospheric Neutrinos ◽  
Neutrino Mass Hierarchy ◽  
Neutrino Telescopes ◽  
Glass Spheres ◽  
Astrophysical Neutrinos ◽  
First Results ◽  
Under Construction ◽  
The Universe

KM3NeT is a network of submarine Cherenkov neutrino telescopes under construction in two different sites in the Mediterranean Sea [1]. The detector at the Italian site, close to the Sicilian coast and named ARCA, will be devoted to the detection of high-energy astrophysical neutrinos coming from sources in the Universe, while the detector at the French site, in the Toulon bay and named ORCA, will exploit atmospheric neutrinos to determine the neutrino mass hierarchy. The telescopes are an array of flexible strings anchored to the sea floor and held close to vertical by submerged buoys. The strings are instrumented with digital optical modules hosted within pressure-resistant glass spheres, each housing 31 3” photomultipliers tubes and the readout electronics. The geometry of the detectors has been adapted to their physics goals. The first calibrations and results of ARCA and ORCA are presented.

Low Energy Neutrino Interactions

1982 ◽  
pp. 241-257
Author(s):  
Henry W. Sobel ◽  
Frederick Reines ◽  
Elaine Pasierb
Keyword(s):  
Low Energy ◽  
Neutrino Interactions ◽  
Energy Neutrino

scholarly journals New vistas in charm production

2019 ◽  
Vol 206 ◽  
pp. 02004
Author(s):  
Antoni Szczurek
Keyword(s):  
Large Deviations ◽  
Transition Probabilities ◽  
High Energy ◽  
Lhcb Collaboration ◽  
Charm Production ◽  
High Energy Neutrino ◽  
Neutrino Production ◽  
Energy Neutrino ◽  
Low Energies

We discuss some new aspects of charm production trigerred by recent observations of the LHCb collaboration. The LHCb collaboration measured small asymmetries in production of D+D−mesons as well as $ D_s^ + D_s^ - $ mesons. Is this related to initial quark/antiquark asymmetries in the proton ? Here we discuss a scenario in which unfavored fragmentations $ q/\bar {q} \to D $ and $ s/\bar {s} \to D_s $ are responsible for the asymmetries. We fix the strength of such fragmentations – transition probabilities, by adjusting to the size of the LHCb asymmetries. This has consequences for production of D mesons in forward directions (large xF ) as well as at low energies. Large asymmetries are predicted then in these regions. We present here some of our predictions. Consequences for high-energy neutrino production in the atmosphere are discussed and quantified. The production of Λc baryon at the LHC is disussed. Large deviations from the independent-parton fragmentation picture are found.

scholarly journals Machine Learning in KM3NeT

2019 ◽  
Vol 207 ◽  
pp. 05004 ◽  
Author(s):  
Chiara De Sio
Keyword(s):  
Machine Learning ◽  
Network Models ◽  
High Energy ◽  
Particle Identification ◽  
Machine Learning Techniques ◽  
Neutrino Interaction ◽  
Mass Hierarchy ◽  
Reconstruction Algorithms ◽  
Neural Network Models ◽  
Neutrino Mass Hierarchy

The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths. The main focus of this work is the application of Convolutional Neural Network models to the tasks of neutrino interaction classification, as well as the estimation of energy and direction of the propagating particles. The performances are also compared to those of the standard reconstruction algorithms used in the Collaboration.

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