THE SPACE TELESCOPE NINA: RESULTS OF A BEAM TEST CALIBRATION

Abstract In June 1998 the telescope NINA will be launched in space on board of the Russian satellite Resource-01 n.4. The main scientific objective of the mission is the study of the anomalous, galactic and solar components of the cosmic rays in the energy interval 10–200 MeV/n. The core of the instrument is a silicon detector whose performances have been tested with a particle beam at the GSI Laboratory in Germany in 1997; we report here on the results obtained during the beam calibration

The small satellite NINA-MITA to study galactic and solar cosmic rays in low-altitude polar orbit

Abstract The satellite MITA, carrying on board the scientific payload NINA-2, was launched on July the 15th, 2000 from the cosmodrome of Plesetsk (Russia) with a Cosmos-3M rocket. The satellite and the payload are currently operating within nominal parameters. NINA-2 is the first scientific payload for the technological flight of the Italian small satellite MITA. The detector used in this mission is identical to the one already flying on the Russian satellite Resurs-O1 n.4 in a 840-km sun-synchronous orbit, but makes use of the extensive computer and telemetry capabilities of MITA bus to improve the active data acquisition time. NINA physics objectives are to study cosmic nuclei from hydrogen to iron in the energy range between 10 MeV/n and 1 GeV/n during the years 2000–2003, that is the solar maximum period. The device is capable of charge identification up to iron with isotope sensitivity up to oxigen. The 87.3 degrees, 460 km altitude polar orbit allows investigations of cosmic rays of solar and galactic origin, so to study long and short term solar transient phenomena, and the study of the trapped radiation at higher geomagnetic cutoff

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

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

Machine learning techniques deep underwater in km3net

Deep in the water of the Mediterranean Sea, the KM3NeT detectors aim at the exploration of the cosmos through the detection of neutrinos and to determine the neutrino mass ordering. Machine learning techniques are widely used to push the performance of the detectors to the limit

Time calibration of the NEutrino Mediterranean Observatory (NEMO)

Large volume Cherenkov detectors are under construction or have been proposed for detection of astrophysical neutrinos Under water or ice. In all such cases, the neutrinos are inferred from the detection of the Cherenkov light emitted by the charged leptons created in neutrino interactions inside or around the apparatus. The event reconstruction is thus based on charge and time measurements performed by a system of widely spaced optical sensors. The time calibration is a very delicate operation for such experiments, as it may directly affect the reconstruction efficiency and pointing capabilities of the apparatus. In this paper, we illustrate the systems under study for the km(3)-scale project NEW (NEutrino Mediterranean Observatory), focusing Oil the implementations for the NEMO Phase-1 and Phase-2 prototyping campaigns. (c) 2008 Elsevier B.V. All rights reserved

Draw me a Neutrino: the first KM3NeT art contest

While the KM3NeT neutrino detector is being deployed in the Mediterranean Sea, the Collaboration launched a contest searching for illustrations of the neutrinos it will detect. The participants in the contest were invited to submit their interpretation of a neutrino, using any technique. More than 500 drawings were submitted from sixteen different countries. The winners were selected by a jury of scientists, artists and science communicators based on the originality and creativity of the drawings, as well as the harmony with the properties and origin of the neutrinos. After announcing the results in an online ceremony with a large international audience, the winning drawings have been put on display in a dedicated KM3NeT Virtual Neutrino Art Centre. In this contribution, we will explain the motivation for the contest and will describe how it was organized. We will also show the winning drawings and present the results of an impact study carried out during the contest