Inference of neutrino flavor evolution through data assimilation and neural differential equations

Rrapaj, Ermal; Patwardhan, Amol V.; Armstrong, Eve; Fuller, George M.

doi:10.1103/physrevd.103.043006

Inference of neutrino flavor evolution through data assimilation and neural differential equations

Journal Article · Sun Feb 07 23:00:00 EST 2021 · Physical Review. D.

DOI:https://doi.org/10.1103/physrevd.103.043006· OSTI ID:1767147

^[1]; ^[2]; Armstrong, Eve ^[3]; Fuller, George M. ^[4]

Univ. of California, Berkeley, CA (United States); Univ. of Minnesota, Minneapolis, MN (United States)
Univ. of California, Berkeley, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Univ. of Washington, Seattle, WA (United States)
New York Inst. of Technology, New York, NY (United States); American Museum of Natural History (AMNH), New York, NY (United States)
Univ. of California, San Diego, CA (United States)

The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for the estimation of model parameters and neutrino flavor evolution histories. Here, we combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for nonlinear dynamical systems. Using this architecture, and a simple two-neutrino-beam, two-flavor model, we compare the performances of nine different optimization algorithms and expand upon previous assessments of the efficacy of inference for tackling problems in flavor evolution. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the small-scale model and allows us to quickly explore both model parameters and initial flavor content. In future work we plan to extend these inference techniques to large numbers of neutrinos.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); Heising-Simons Foundation

Grant/Contract Number:: AC02-76SF00515; SC0018297

OSTI ID:: 1767147

Report Number(s):: SLAC-PUB--17563

Journal Information:: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 4 Vol. 103; ISSN 2470-0010

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Inference of neutrino flavor evolution through data assimilation and neural differential equations

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