In CLEAN (Cryogenic Low Energy Astrophysics with Noble gases), a proposed neutrino and dark matter detector, background discrimination is possible if one can determine the location of an event with high accuracy. Here, we develop spatial methods for event reconstruction, and study their performance in computer experiments. We simulate ionizing radiation events that produce multiple scintillation photons within a spherical detection volume filled with liquid neon. We estimate the radial location of a particular ionizing radiation event based on the observed count data corresponding to that event. The count data is collected by detectors mounted at the spherical boundary of the detection volume. We neglect absorption, but account for Rayleigh scattering. To account for wavelength-shifting of the scintillation light, we assume that photons are absorbed and re-emitted at the detectors. In our study, the detectors incompletely cover the surface area of the sphere. In one method, we estimate the radial location of the event by maximizing the approximate Poisson likelihood of the observed count data. To correct for scattering and wavelength-shifting, we adjust this estimate using a polynomial calibration model. In the second method, we predict the radial location of the event as a polynomial function of the magnitude of the Centroid of the observed count data. The polynomial calibration models are constructed from calibration (training) data. In general, the Maximum Likelihood method estimate is more accurate than the centroid method estimate. We estimate the expected number of photons emitted by the event by a Maximum Likelihood method and a simple method based on the ratio of the number of detected photons and a detection probability.
Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors and Associated Equipment
and McKinsey, D.
Spatial Methods for Event Reconstruction in CLEAN, Nuclear Instruments & Methods in Physics Research Section A-Accelerators Spectrometers Detectors and Associated Equipment, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=50793
(Accessed March 1, 2024)