Speaker
Description
We present a potential improvement over the standard method developed to determine antineutrino directionality in inverse-beta-decay detectors. The previously developed method in monolithic and segmented detectors underestimated angular uncertainty in the low-count regime. We will cover our latest publication on a new directionality algorithm and our current work in progress.
We have developed a new algorithm based on a measure of "distance'' between two matrices. We identify an optimal segmentation scale in the low-count regime. We also discuss the shortcomings of the conventional method and how this knowledge can be applied to segmented detectors, hybrid designs, and generalized validation, agnostic to the physics of detector design. We report findings for our research in reactor-antineutrino directionality, and emphasize that the algorithm has broad applications in machine learning whenever one desires computationally efficient 2D and 3D pattern-matching.