Astronomy and Astrophysics – Astrophysics
Scientific paper
2004-01-31
Astropart.Phys. 22 (2005) 355-368
Astronomy and Astrophysics
Astrophysics
21 pages, 3 figures. Submitted to Astroparticle Physics
Scientific paper
10.1016/j.astropartphys.2004.10.
This article describes CLEAN, an approach to the detection of low-energy solar neutrinos and neutrinos released from supernovae. The CLEAN concept is based on the detection of elastic scattering events (neutrino-electron scattering and neutrino-nuclear scattering) in liquified noble gases such as liquid helium, liquid neon, and liquid xenon, all of which scintillate brightly in the ultraviolet. Key to the CLEAN technique is the use of a thin film of wavelength-shifting fluor to convert the ultraviolet scintillation light to the visible. This allows the same liquid to be used as both a passive shielding medium and an active self-shielding detector, allowing lower intrinsic radioactive backgrounds at low energies. Liquid neon is a particularly promising medium for CLEAN. Because liquid neon has a high scintillation yield, has no long-lived radioactive isotopes, and can be easily purified by use of cold traps, it is an ideal medium for the detection of rare nuclear events. In addition, neon is inexpensive, dense, and transparent to its own scintillation light, making it practical for use in a large self-shielding apparatus. Monte Carlo simulations of gamma ray backgrounds have been performed assuming liquid neon as both shielding and detection medium. Gamma ray events occur with high probability in the outer parts of the detector. In contrast, neutrino scattering events occur uniformly throughout the detector. We discriminate background gamma ray events from events of interest based on a spatial Maximum Likelihood method estimate of event location. Background estimates for CLEAN are presented, as well as an evaluation of the sensitivity of the detector for $p-p$ neutrinos. Given these simulations, the physics potential of the CLEAN approach is evaluated.
Coakley Kevin J.
McKinsey Daniel N.
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