Speaker
Description
"We present a potential improvement over the standard method developed to determine antineutrino directionality in inverse-beta-decay detectors via a new directionality algorithm utilizing the Frobenius norm for the ``distance'' between two matrices. The previously developed method in monolithic and segmented detectors underestimated angular uncertainty in the low-count regime. We discuss the shortcomings of the conventional method and how this knowledge can be applied to generalized validation, agnostic to the physics of detector design. We will cover our latest publication and our current follow-up work employing novel statistical and machine learning methods. We emphasize that the algorithm has broad applications in machine learning whenever one desires computationally efficient 2D pattern-matching.
To further demonstrate the intersection of neutrino physics and advanced computation, we will also discuss our parallel project calculating torsion's effect on neutrinos from core collapse supernovae (CCSN). While GLoBES is highly effective for calculating MSW non-standard interactions (NSI) during terrestrial transit, it relies on linear transfer matrices with a constant Hamiltonian over discrete spatial steps. This framework breaks down in dense CCSN interiors; therefore, we overcome these limitations by natively integrating the Liouville-von Neumann equations as a continuous initial value problem with a dynamic Hamiltonian to accurately calculate NSI terms, such as non-linear neutrino self-interactions and torsion.
Together, these efforts showcase how developing physics-informed machine learning and algorithmic engines can overcome standard framework limitations in both detector pattern-matching and non-linear environments."id over a greatly expanded phase space.
| Contribution types | Long talk (30min + 10min Q/A) |
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