Architecture and performance of the KM3NeT front-end firmware

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'Istruzione, 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. 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 infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS)Commission Europeenne (FEDER fund), FranceCommission Europeenne (Marie Curie Program), 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-GSRTIstituto Nazionale di Fisica Nucleare (INFN) NAT-NET 2017W4HA7SMinistry of Education, Universities and Research (MIUR) NAT-NET 2017W4HA7SPRIN 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 (MCIU/FEDER), 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 program, Spain GRISOLIA/2018/119Generalitat Valenciana: GenT program, Spain CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program, Spain 71367

Determining the neutrino mass ordering and oscillation parameters with KM3NeT/ORCA

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. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034 and CIDEGENT/2019/043) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The next generation of water Cherenkov neutrino telescopes in the Mediterranean Sea are under construction offshore France (KM3NeT/ORCA) and Sicily (KM3NeT/ARCA). The KM3NeT/ORCA detector features an energy detection threshold which allows to collect atmospheric neutrinos to study flavour oscillation. This paper reports the KM3NeT/ORCA sensitivity to this phenomenon. The event reconstruction, selection and classification are described. The sensitivity to determine the neutrino mass ordering was evaluated and found to be 4.4 sigma if the true ordering is normal and 2.3 sigma if inverted, after 3 years of data taking. The precision to measure Delta m(32)(2) and theta(23) were also estimated and found to be 85.10(-6) eV(2) and ((+1.9)(-3.1))degrees for normal neutrino mass ordering and, 75.10(-6) eV(2) and ((+2.0)(-7.0))degrees for inverted ordering. Finally, a unitarity test of the leptonic mixing matrix by measuring the rate of tau neutrinos is described. Three years of data taking were found to be sufficient to exclude (nu)over-left-right-arrow tau event rate variations larger than 20% at 3 sigma level.French National Research Agency (ANR) ANR-15-CE31-0020 Centre National de la Recherche Scientifique (CNRS) Commission Europeenne (FEDER fund), France Institut Universitaire de France (IUF), France LabEx UnivEarthS, France ANR-10-LABX-0023 ANR-18-IDEX-0001 Paris Ile-de-France Region, FranceShota Rustaveli National Science Foundation of Georgia (SRNSFG), Georgia FR-18-1268German Research Foundation (DFG)The General Secretariat of Research and Technology (GSRT), GreeceIstituto 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 GovernmentThe National 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-C44 Generalitat Valenciana: Grisolia program, Spain GRISOLIA/2018/119 La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program, Spain 713673Commission Europeenne (Marie Curie Program), FranceGeneralitat Valenciana: GenT programs, Spain CIDEGENT/2018/034 CIDEGENT/2019/043Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Severo Ochoa Centre of ExcellenceMinisterio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): MultiDark Consolider (MCIU)Junta de Andalucia European Commission SOMM17/6104/UG

Smart Solar Micro-exchangers for Sustainable Mobility of University Camps

Publicado el resumen en: https://www.wmcaus.org/files/WMCAUS2020_Book.pdf. Pendiente de publicación de las contribuciones en IOP Conference Series: Materials Science and Engineering.A significant number of universities have several campuses located in urban or rural settings, or with scattered university buildings that require the use of means of transportation. This implies the mobility and potential displacement of a large community of students, professors and researchers. The use of electric bicycles (e-bikes) is an intermediate alternative between the bicycle and electric cars. It can be an important stimulus for the promotion of the decarbonisation of the University Campus, avoiding the traffic congestion and reducing space requirements for parking. This paper presents the smart solar micro-exchanger model managed through a sustainable mobility web platform, applied to the case study of the University of Malaga (Spain). It is a solar charging station for e-bike, whose design is based on the principles of solar architecture (providing great security to e-bike). It managed by a web platform and app that allows the user to make reservations and learn about the savings in CO2 emissions. The system allows performing an aerobic sports activity without sweating problems when you reach the job. The platform also incorporates a database of quiet and safe routes for e-bike users.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

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

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 de tector 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

KM3NeT front-end and readout electronics system: hardware, firmware, and software

he KM3NeT research infrastructure being built at the bottom of the Mediterranean Sea will host water-Cherenkov telescopes for the detection of cosmic neutrinos. The neutrino telescopes will consist of large volume three-dimensional grids of optical modules to detect the Cherenkov light from charged particles produced by neutrino-induced interactions. Each optical module houses 31 3-in. photomultiplier tubes, instrumentation for calibration of the photomultiplier signal and positioning of the optical module, and all associated electronics boards. By design, the total electrical power consumption of an optical module has been capped at seven Watts. We present an overview of the front-end and readout electronics system inside the optical module, which has been designed for a 1-ns synchronization between the clocks of all optical modules in the grid during a life time of at least 20 years

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

KM3NeT Detection Unit Line Fit reconstruction using positioning sensors data

The KM3NeT collaboration is constructing two large neutrino detectors in the Mediterranean Sea: KM3NeT/ARCA, located near Sicily and aiming at neutrino astronomy, and KM3NeT/ORCA, located near Toulon and designed for neutrino oscillation studies. The two detectors, together, will have hundreds of Detection Units (DUs) with 18 Digital Optical Modules (DOMs) maintained vertical by buoyancy, forming a large 3D optical array for detecting the Cherenkov light produced by particle produced in neutrino interactions. To properly reconstruct the direction of the incoming neutrino, the position of the DOMs must be known precisely with an accuracy of less than 10 cm, and since the DUs are affected by sea current the position will be measured every 10 minutes. For this purpose, there are acoustic and orientation sensors inside the DOMs. An Attitude Heading Reference System (AHRS) chip provides the components values of the Acceleration and Magnetic field in the DOM, from which it is possible to calculate Yaw, Pitch, and Roll for each floor of the line. A piezo sensor detects the signals from fixed acoustic emitters on the sea floor, so to position it by trilateration. Data from these sensors are used as an input to reconstruct the shape of the entire line based on a DU Line Fit mechanical model. This poster presents an overview of the KM3NeT monitoring system, as well as the line fit model and its results