The REINFORCE Project: Inviting Citizen Scientists to analyse KM3NeT data

KM3NeT Collaboration, REINFORCE Consortium

Research output: Contribution to journalConference articlepeer-review

Abstract

Large research infrastructures have opened new observational windows, allowing us to study the structure of matter up to the entire Universe. However, society hardly observes these developments through education and outreach activities. This induces a gap between frontier science and society that may create misconceptions about the content, context, and mission of public funded science. In this context, the main goal of the European Union’s Horizon 2020 "Science with and for Society" REINFORCE project (REsearch INfrastructure FOR Citizens in Europe) is to minimize the knowledge gap between large research infrastructures and society through Citizen Science. A series of activities is being developed on the Zooniverse platform, in four main fields of frontier physics involving large research infrastructures: gravitational waves with the VIRGO interferometer, particle physics with the ATLAS detector at LHC, neutrinos with the KM3NeT telescope, and cosmic rays at the interface of geoscience and archeology. Using real and simulated data, Citizen Scientists will help building a better understanding of the impact of the environment on these very high precision detectors as well as creating new knowledge. This poster describes REINFORCE, with a special emphasis on the Deep Sea Hunter demonstrator involving the KM3NeT neutrino telescope, in order to show practical examples of Citizen Science activities that will be proposed through the project.

Original languageEnglish
Article number1392
JournalProceedings of Science
Volume395
Publication statusPublished - 18 Mar 2022
Event37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany
Duration: 12 Jul 202123 Jul 2021

ASJC Scopus subject areas

  • Multidisciplinary

Fingerprint

Dive into the research topics of 'The REINFORCE Project: Inviting Citizen Scientists to analyse KM3NeT data'. Together they form a unique fingerprint.

Cite this