91 research outputs found
The KM3NeT potential for the next core-collapse supernova observation with neutrinos
The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education Scientific Research and Professional Training, ICTP through Grant AF-13, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISO-LIA/2018/119) and GenT (ref. CIDEGENT/2018/034 and CIDE-GENT/2019/043) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain. This work has also received funding from the European Union'sHorizon 2020 research and innovation program under Grant agreement no 739560.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.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)Commission Europeenne, FranceInstitut Universitaire de France (IUF), FranceLabEx UnivEarthS, France ANR-10-LABX-0023
ANR-18-IDEX-0001Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTGreek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)PRIN 2017 program, Italy NAT-NET 2017W4HA7SMinistry of Higher Education Scientific Research and Professional Training, ICTP, Morocco AF-13Netherlands Organization for Scientific Research (NWO)Netherlands GovernmentNational Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento, Spain PGC2018-096663-B-C41
PGC2018-096663-A-C42
PGC2018-096663-B-C43
PGC2018-096663-B-C44Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), SpainJunta de Andalucia
European Commission SOMM17/6104/UGRGeneralitat Valenciana: Grisolia, Spain GRISO-LIA/2018/119
GenT program, Spain CIDEGENT/2018/034
CIDE-GENT/2019/043La Caixa Foundation LCF/BQ/IN17/11620019
EU: MSC program, Spain 713673European Commission 73956
Deep-sea deployment of the KM3NeT neutrino telescope detection units by self-unrolling
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®
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.French National Research Agency (ANR)
ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS
ANR-10-LABX-0023
ANR-18-IDEX-0001Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia
FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN Italy
NAT-NET 2017W4HA7SMinistry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)
Netherlands GovernmentNational Science Center, Poland
National Science Centre, Poland
2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovación, Investigación y Universidades (MCIU): Programa Estatal de Generación de Conocimiento (MCIU/FEDER)
PGC2018-096663-B-C41
PGC2018-096663-B-A-C42
PGC2018-096663-B-BC43
PGC2018-096663-B-B-C44Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucía
SOMM17/6104/UGRGeneralitat Valenciana
GRISOLIA/2018/119
CIDEGENT/2018/034La Caixa Foundation
LCF/BQ/IN17/11620019EU: MSC program, Spain
71367
Event reconstruction for KM3NeT/ORCA using convolutional neural networks
The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.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.French National Research Agency (ANR)
ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS
ANR-10-LABX-0023
ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia
FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)
Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland
2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades
PGC2018-096663-B-C41
A-C42
B-C43
B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia
SOMM17/6104/UGRGeneralitat Valenciana: Grisolia
GRISOLIA/2018/119
CIDEGENT/2018/034La Caixa Foundation
LCF/BQ/IN17/11620019EU: MSC program
71367
Implementation and first results of the KM3NeT real-time core-collapse supernova neutrino search
The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MIUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education Scientific Research and Professional Training, ICTP through Grant AF-13, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Generalitat Valenciana: Prometeo (PROMETEO/2020/019), Grisolia (ref. GRISOLIA/2018/119) and GenT (refs. CIDEGENT/2018/034, /2019/043, /2020/049) programs, Junta de Andalucia (ref. A-FQM-053-UGR18), La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 101025085), Spain.The KM3NeT research infrastructure is unconstruction
in the Mediterranean Sea. KM3NeT will study
atmospheric and astrophysical neutrinos with two multipurpose
neutrino detectors, ARCA and ORCA, primarily
aimed at GeV–PeV neutrinos. Thanks to the multiphotomultiplier
tube design of the digital optical modules,
KM3NeT is capable of detecting the neutrino burst from
a Galactic or near-Galactic core-collapse supernova. This potential is already exploitable with the first detection units
deployed in the sea. This paper describes the real-time implementation
of the supernova neutrino search, operating on the
two KM3NeT detectors since the first months of 2019. A
quasi-online astronomy analysis is introduced to study the
time profile of the detected neutrinos for especially significant
events. Themechanism of generation and distribution of
alerts, aswell as the integration into theSNEWSandSNEWS
2.0 global alert systems, are described. The approach for the
follow-up of external alerts with a search for a neutrino excess
in the archival data is defined. Finally, an overviewof the current
detector capabilities and a report after the first two years
of operation are given.French National Research Agency (ANR)European Commission ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)Commission EuropeenneInstitut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023
ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR)PRIN 2017 program, Italy NAT-NET 2017W4HA7SMinistry of Higher Education Scientific Research and Professional Training, ICTP, Morocco AF-13Netherlands Organization for Scientific Research (NWO)
Netherlands GovernmentNational Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento PGC2018-096663-B-C41
PGC2018-096663-A-C42
PGC2018-096663-B-C43
PGC2018-096663-B-C44Generalitat Valenciana PROMETEO/2020/019Grisolia program GRISOLIA/2018/119
CIDEGENT/2018/034Junta de Andalucia A-FQM-053-UGR18La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 101025085Paris Ile-de-France Region, FranceGenT program CIDEGENT/2018/034
CIDEGENT/2019/043
CIDEGENT/2020/04
Probing invisible neutrino decay with KM3NeT-ORCA
In the era of precision measurements of the neutrino oscillation parameters,
upcoming neutrino experiments will also be sensitive to physics beyond the
Standard Model. KM3NeT/ORCA is a neutrino detector optimised for measuring
atmospheric neutrinos from a few GeV to around 100 GeV. In this paper, the
sensitivity of the KM3NeT/ORCA detector to neutrino decay has been explored. A
three-flavour neutrino oscillation scenario, where the third neutrino mass
state decays into an invisible state, e.g. a sterile neutrino, is
considered. We find that KM3NeT/ORCA would be sensitive to invisible neutrino
decays with ~ at confidence
level, assuming true normal ordering. Finally, the impact of neutrino decay on
the precision of KM3NeT/ORCA measurements for ,
and mass ordering have been studied. No significant effect of neutrino decay on
the sensitivity to these measurements has been found.Comment: 27 pages, 14 figures, bibliography updated, typos correcte
Embedded Software of the KM3NeT Central Logic Board
The KM3NeT Collaboration is building and operating two deep sea neutrino
telescopes at the bottom of the Mediterranean Sea. The telescopes consist of
latices of photomultiplier tubes housed in pressure-resistant glass spheres,
called digital optical modules and arranged in vertical detection units. The
two main scientific goals are the determination of the neutrino mass ordering
and the discovery and observation of high-energy neutrino sources in the
Universe. Neutrinos are detected via the Cherenkov light, which is induced by
charged particles originated in neutrino interactions. The photomultiplier
tubes convert the Cherenkov light into electrical signals that are acquired and
timestamped by the acquisition electronics. Each optical module houses the
acquisition electronics for collecting and timestamping the photomultiplier
signals with one nanosecond accuracy. Once finished, the two telescopes will
have installed more than six thousand optical acquisition nodes, completing one
of the more complex networks in the world in terms of operation and
synchronization. The embedded software running in the acquisition nodes has
been designed to provide a framework that will operate with different hardware
versions and functionalities. The hardware will not be accessible once in
operation, which complicates the embedded software architecture. The embedded
software provides a set of tools to facilitate remote manageability of the
deployed hardware, including safe reconfiguration of the firmware. This paper
presents the architecture and the techniques, methods and implementation of the
embedded software running in the acquisition nodes of the KM3NeT neutrino
telescopes
Prospects for combined analyses of hadronic emission from -ray sources in the Milky Way with CTA and KM3NeT
The Cherenkov Telescope Array and the KM3NeT neutrino telescopes are major
upcoming facilities in the fields of -ray and neutrino astronomy,
respectively. Possible simultaneous production of rays and neutrinos
in astrophysical accelerators of cosmic-ray nuclei motivates a combination of
their data. We assess the potential of a combined analysis of CTA and KM3NeT
data to determine the contribution of hadronic emission processes in known
Galactic -ray emitters, comparing this result to the cases of two
separate analyses. In doing so, we demonstrate the capability of Gammapy, an
open-source software package for the analysis of -ray data, to also
process data from neutrino telescopes. For a selection of prototypical
-ray sources within our Galaxy, we obtain models for primary proton and
electron spectra in the hadronic and leptonic emission scenario, respectively,
by fitting published -ray spectra. Using these models and instrument
response functions for both detectors, we employ the Gammapy package to
generate pseudo data sets, where we assume 200 hours of CTA observations and 10
years of KM3NeT detector operation. We then apply a three-dimensional binned
likelihood analysis to these data sets, separately for each instrument and
jointly for both. We find that the largest benefit of the combined analysis
lies in the possibility of a consistent modelling of the -ray and
neutrino emission. Assuming a purely leptonic scenario as input, we obtain, for
the most favourable source, an average expected 68% credible interval that
constrains the contribution of hadronic processes to the observed -ray
emission to below 15%.Comment: 18 pages, 15 figures. Submitted to journa
Event reconstruction for KM3NeT/ORCA using convolutional neural networks
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
Implementation and first results of the KM3NeT real-time core-collapse supernova neutrino search
The KM3NeT research infrastructure is unconstruction in the Mediterranean Sea. KM3NeT will study atmospheric and astrophysical neutrinos with two multi-purpose neutrino detectors, ARCA and ORCA, primarily aimed at GeV–PeV neutrinos. Thanks to the multi-photomultiplier tube design of the digital optical modules, KM3NeT is capable of detecting the neutrino burst from a Galactic or near-Galactic core-collapse supernova. This potential is already exploitable with the first detection units deployed in the sea. This paper describes the real-time implementation of the supernova neutrino search, operating on the two KM3NeT detectors since the first months of 2019. A quasi-online astronomy analysis is introduced to study the time profile of the detected neutrinos for especially significant events. The mechanism of generation and distribution of alerts, as well as the integration into the SNEWS and SNEWS 2.0 global alert systems, are described. The approach for the follow-up of external alerts with a search for a neutrino excess in the archival data is defined. Finally, an overview of the current detector capabilities and a report after the first two years of operation are given
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