Papers

Peer-reviewed
2017

Nuclear emulsion techniques for muography

ANNALS OF GEOPHYSICS
  • Cristiano Bozza
  • Lucia Consiglio
  • Nicola D'Ambrosio
  • Giovanni De Lellis
  • Chiara De Sio
  • Seigo Miyamoto
  • Ryuichi Nishiyama
  • Chiara Sirignano
  • Simona Maria Stellacci
  • Paolo Strolin
  • Hiroyuki K. M. Tanaka
  • Valeri Tioukov
  • Display all

Volume
60
Number
1
Language
English
Publishing type
Research paper (scientific journal)
DOI
10.4401/ag-7384
Publisher
IST NAZIONALE DI GEOFISICA E VULCANOLOGIA

Nuclear emulsions are currently being used in the field of muography, more specifically muon radiography of volcanic edifices and fault regions. The peculiar features of such detector for cosmic muons demand appropriate data processing and analysis techniques. The paper shows the current development status of readout devices and analysis techniques developed by some research groups that established a collaborative network in Italy and Japan. An overview is given of nuclear emulsion-based detectors, from the detection principles to detector operation and set-up techniques, in connection with the expectations in terms of geophysics information. Two systems for readout are presented, one developed in the first decade of the 21st century and one that is entering duty now. The evolution in terms of data quality and speed is discussed. Finally, the most relevant data processing steps that allow working out muon absorption maps from nuclear emulsion data are described.

Link information
DOI
https://doi.org/10.4401/ag-7384
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000408568000009&DestApp=WOS_CPL
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047498492&origin=inward Open access
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85047498492&origin=inward
ID information
  • DOI : 10.4401/ag-7384
  • ISSN : 1593-5213
  • eISSN : 2037-416X
  • SCOPUS ID : 85047498492
  • Web of Science ID : WOS:000408568000009

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