Published January 2025 | Published
Journal Article

Design and assembly status overview of the Mu2e electromagnetic calorimeter mechanical structures

Abstract

The “muon-to-electron conversion” (Mu2e) experiment at Fermilab will search for the Charged Lepton Flavour Violating neutrino-less coherent conversion of a muon into an electron in the field of an aluminum nucleus. The observation of this process would be the unambiguous evidence of physics beyond the Standard Model. The detector has been designed as a state-of-the-art crystal calorimeter and employs 1348 pure Cesium Iodide (CsI) crystals readout by UV-extended silicon photosensors and fast front-end and digitization electronics. A design consisting of two identical annular matrices (named “disks”) positioned at the relative distance of 70 cm, downstream the aluminum target along the muon beamline, satisfies the Mu2e physics requirements. The hostile Mu2e operational conditions, in terms of radiation levels (total ionizing dose of 90 krad and a neutron fluence of 5x10¹²n/cm²@1MeVeq(Si)/y), magnetic field intensity (1 T) and vacuum level (10⁻⁴ Torr) have posed tight constraints on the design of the detector mechanical structures and materials choice. The support structure of the two 674 crystal matrices employs two aluminum hollow rings and parts made of open-cell vacuum-compatible carbon fiber. The photosensors and service front-end electronics for each crystal are assembled in a unique mechanical unit inserted in a machined copper holder. The 674 units are supported by a machined plate made of vacuum-compatible plastic material. The plate also integrates the cooling system made of a network of copper lines flowing a low temperature radiation-hard fluid and placed in thermal contact with the copper holders. The data acquisition electronics is hosted in aluminum custom crates positioned on the external lateral surface of the two disks. The crates also integrate the electronics cooling system.

Copyright and License

© 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Acknowledgement

We are grateful for the vital contributions of the Fermilab staff and the technical staff of the participating institutions. 

Funding

This work was supported by the US Department of Energy; the Istituto Nazionale di Fisica Nucleare, Italy; the Science and Technology Facilities Council, UK; the Ministry of Education and Science, Russian Federation; the National Science Foundation, USA; the Thousand Talents Plan, China; the Helmholtz Association, Germany; and the EU Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement No. 734303822185858199101003460101006726. This document was prepared by members of the Mu2e Collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359.

Conflict of Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The Mu2e Collaboration reports financial support was provided by US Department of Energy. INFN Personnel and Associates in the Mu2e Collaboration reports financial support was provided by European Union. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional details

Created:
November 23, 2024
Modified:
November 23, 2024