6062 research outputs found
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SI-GaAs wafers as resistive electrode for high-rate RPC with very low dark count rate
Full text link<p>Presented by A.Rocchi at RPC2022 conference XVI Workshop on Resistive Plate Chambers and Related Detectors Sep 26 – 30, 2022 , <span>CERN</span></p>
Utilizzo dei dati satellitari - Esercitazione
No full text<p>These exercises (in Italian) are related to the lesson on the following topic: Using Satellite Data, within the annual course 'Introduction to Remote Sensing and the use of satellite data for environmental monitoring' organized by ISPRA within its SNPA activities.</p>
INFN Technology: Cultural Heritage
No full text<p>Promo video about INFN's technologies in thecultural heritage field.</p>
<p>3 versions available:</p>
<ul>
<li>full length</li>
<li>60 seconds</li>
<li>30 seconds</li>
</ul>
Frankfurt prototype neutron detector array dataset 1/2
No full text<p>Raw data collected in July 2022 at the Frankfurt "FRANZ" neutron beam facility, for the characterisation of a prototype neutron detector array for future nuclear astrophysics studies with SHADES (Scintillator-3He Array for Deep-underground Experiments on the S-process). </p>
<p>This is repository 1 of 2, repository 2 of 2 is available at DOI: 10.15161/oar.it/bhkhq-8ar22</p>
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Booklet of the Italian Research Day in the World 2025 - Lund, 15-16 May 2025
No full text<p>The Italian Research Day in the World (GRIM) is an initiative by the Italian Ministry of University and Research, and the Ministry of Foreign Affairs. Organized by the Italian Embassies all over the world, it aims at highlighting the contributions of Italian researchers working abroad and at showcasing Italy's commitment to science, technology, and innovation to promote international cooperation. </p>
<p>The document collects the contribution of the events organized this year for the first time out of Stockholm, in the Skåne region, joining key actors from academia, industry, the innovation ecosystems of the region, and the regional administration to emphasize the role of policymakers for research and of innovation. </p>
<p>This initiative brought together scientists engaged in diverse research areas, and representatives from Mind Innovation District, the Lund and Helsingborg Innovation districts and the Malmö Generate district, all having in common the interest to promote the dialogue, the research, and the innovation in Italy, Sweden and in Europe. The initiative was timely because addressing STEM issues in the framework of innovation is becoming a fundamental aspect of policies in the EU and globally, and we expect that STEM minds will play a larger role in the future.</p>
Simulation Study of Photon-to-Digital Converter (PDC) Timing Specifications for LoLX Experiment
No full textThe Light-only Liquid Xenon (LoLX) experiment is a prototype detector aimed at studying liquid xenon (LXe) light properties and various photodetection technologies. LoLX is also aimed to quantify LXe's time resolution as a potential scintillator for 10-ps time-of-flight positron emission tomography (TOF-PET). Another key goal of LoLX is to perform a time-based separation of Cerenkov and scintillation photons for new background rejection methods in LXe experiments. To achieve this separation, LoLX is set to be equipped with photon-to-digital converters (PDCs), a photosensor type that can provide a timestamp for each observed photon. To guide the PDC design, we explore the requirements and potential outcomes for time-based Cerenkov separation. We use a PDC simulator, whose input is the light information from the Geant4-based LoLX simulation model, and evaluate the separation quality against time-to-digital converter (TDC) parameters of the PDCs. Compared with the current filter-based approach, the simulations predict a few different configurations that offer a Cerenkov separation level increase from 50% to 66% when using PDCs and time-based separation. A separation of 65% is also achievable with just 16 TDCs for 14 400 micro-cells per PDC, or one TDC per 2.25 mm2. These simulation results will lead to a specification guide for the upcoming PDC design as well as expected results to compare against future PDC-based experimental measurements. In the longer term, the overall LoLX results will assist large LXe-based experiments and motivate the assembly of an LXe-based TOF-PET demonstrator system
Note Illustrative della Carta geologica d'Italia alla scala 1:50.000, F. 549 Muravera, Servizio Geologico d'Italia - ISPRA
No full text<p>Note illustrative redatte per il Foglio geologico n. 549 Muravera della Carta Geologica d'Italia alla scala 1:50.000. 146 pp.</p>
Banca dati geologica F. 446-447 Napoli scala 1:25.000
Full text link<p>Banca Dati alla scala 1:25.000 della Carta Geologica prodotta nell'ambito del Progetto CARG in formato GeoPackage. I layer presenti si riferiscono alle Unità cartografabili geologiche, agli Elementi geomorfologici e alle Risorse e Prospezioni. Gli strati informativi sono rappresentati in accordo con la simbologia prevista dalla normativa del Progetto CARG.</p>
Dataset related to article \"Financial and numerical abilities: patterns of dissociation in neurological and psychiatric diseases\
No full text<p>-</p>
Extending Cosmic Ray Background in Space Experiments using Generative Adversarial Networks
Full text link<p>Cosmic rays (CR) reaching telescope detectors in outer space are known<br>\nto induce glitches and background noise. The presence of CR noise significantly<br>\ninfluenced Cosmic Microwave Background (CMB) experiments, like Planck and<br>\nLiteBIRD, which have a long exposition and hard shelling or filtering. In order to<br>\naddress this challenge, it is imperative to accurately simulate the CR background<br>\nthroughout the duration of LiteBIRD’s three-year mission. However, state-of-the-art<br>\nMonte Carlo (MC) simulations are extremely computational expensive, typically<br>\nrequiring 30 times the simulated period. We present the Cosmic Rays Artificial<br>\nBackground (CRAB) code, extending MC simulations with Generative Adversarial<br>\nNetworks (GAN). By leveraging GANs, we can efficiently generate a sufficient<br>\nnumber of genuine, statistically independent images, unlike traditional noise analysis<br>\ntechniques combined with template expansion methods.</p>