A. P. Smith | Searcharxiv

arXiv2020

First Measurement of the Asymmetry and the Gerasimov-Drell-Hearn Integrand from $\vec{{^3}He}(\vecγ,p)d$ reaction at the Incident Photon Energy of 29 MeV

The first measurement of the $\vec{^3He}(\vecγ,p)d$ process was performed at the High Intensity $γ$-ray Source (HI$γ$S) facility at Triangle Universities Nuclear Laboratory (TUNL) using a circularly polarized, monoenergetic $γ$-ray beam and a longitudinally polarized $^3$He target. The spin-dependent asymmetry and the contribution from the two-body photodisintegration to the $^3$He Gerasimov-Drell-Hearn integrand are extracted and compared with state-of-the-art three-nucleon system calculations at the incident photon energy of 29.0 MeV. The data are in general agreement with the various theoretical predictions based on the Siegert theorem or on explicit inclusion of meson-exchange currents.

nucl-ex↗

arXiv2018

Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes

Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future experiments. Merging mathematical theory with empirical measurements in a statistically coherent way is critical and challenges abound, e.g.,: ill-posedness of the parameter estimation problem, proper regularization and incorporation of prior knowledge, and computational limitations on full uncertainty qualification. To address these issues, we propose a new method for learning parameterized dynamical systems from data. In many ways, our proposal turns the canonical framework on its head. We first fit a surrogate stochastic process to observational data, enforcing prior knowledge (e.g., smoothness), and coping with challenging data features like heteroskedasticity, heavy tails and censoring. Then, samples of the stochastic process are used as "surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. An attractive feature of this approach is that it is fully Bayesian and simultaneously parallelizable. We demonstrate the advantages of our new approach on a predator prey simulation study and on a real world application involving within-host influenza virus infection data paired with a viral kinetic model.

stat.ME↗