MRI: Construction of One Layer of a Highly Efficient Neutron Detector to Study Neutron-Rich Rare Isotopes at the NSCL

  • Finck, Joseph J.E. (PI)

Grant Details

Description

The consortium between Michigan State University and Florida State

University proposes the construction of a highly efficient,

large-area neutron detector for the detection of high-energy

neutrons to be used in experiments with fast rare isotopes at the

National Superconducting Cyclotron Laboratory (NSCL). This

consortium is joined by Ball State University, Central Michigan

University, Concordia College in Moorhead, Minn., Hope College,

Indiana University at South Bend, Millikin University, Western

Michigan University, and Westmont College, each of which will

assemble and test one complete layer of the detector.

The proposed detector consists of 144 horizontal blocks of plastic

scintillator arranged in 9 layers of 16 detectors each, covering an

area of 2.0 m wide by 1.6 m high. The detector is position

sensitive and features multi-hit capability. The addition of

passive iron converters enhances the detection efficiency for

neutrons with energies above 100 MeV for an average efficiency of up

to 70%. The high detection efficiency will allow the investigation

of very neutron-rich nuclei that can only be produced with small

intensities.

The detector will be used in connection with the new sweeper magnet

with its focal plane detectors in stand-alone mode as well as with

the combination of sweeper magnet and the S800 magnetic

spectrograph. It will therefore be essential for the experimental

program at the coupled cyclotron facility. The detector can be

optimized for even higher beam energies with only minor

modifications due to its modular design. It could be the first

detector to be used for fast fragmentation beams at the Rare Isotope.

StatusFinished
Effective start/end date09/1/0108/31/04

Funding

  • National Science Foundation: $91,126.00

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.