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Arlee Shelby

Publications and source records attributed to Arlee Shelby.

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Characterization of Low-energy Ionization Signals in Silicon Detectors for the Nab Experiment

The Nab (Neutron a b) experiment is designed to measure the beta-antineutrino angular correlation in free neutron $β$ decay with an ultimate precision goal of 0.1%, providing input for tests of Cabibbo-Kobayashi-Maskawa (CKM) matrix unitarity. This measurement is performed via detection of electrons and protons in delayed coincidence using custom large-area segmented silicon detectors. We present the characterization of one such detector system to establish the proton energy and timing response, using a dedicated proton accelerator. The detected proton peak was studied for 25 keV, 30 keV, and 35 keV incident protons on a set of detector segments and multiple cooling cycles over a one year period. Ionization losses were consistent with models of the detector dead layer with thicknesses less than 100 nm. The detected proton peak was stable within the uncertainty from energy calibration (0.25 keV). The rise times of detector pulses from $^{109}$Cd and $^{113}$Sn conversion electron sources were used to extract the impurity density profile and establish a precise model for the detector timing response. The observed impurity density profile varied from $(2 \pm 2) \times 10^9$ cm$^{-3}$ at the center to $(26 \pm 2) \times 10^9$ cm$^{-3}$ at the edge. This impurity density profile was then used to characterize systematic effects in proton time-of-flight measurements due to detector pulse-shape effects; the resultant proton timing systematic uncertainties were below 0.3 ns, which is sufficient for the Nab experiment.

physics.ins-det

Quantum states from normalizing flows

We introduce an architecture for neural quantum states for many-body quantum-mechanical systems, based on normalizing flows. The use of normalizing flows enables efficient uncorrelated sampling of configurations from the probability distribution defined by the wavefunction, mitigating a major cost of using neural states in simulation. We demonstrate the use of this architecture for both ground-state preparation (for self-interacting particles in a harmonic trap) and real-time evolution (for one-dimensional tunneling). Finally, we detail a procedure for obtaining rigorous estimates of the systematic error when using neural states to approximate quantum evolution.

quant-ph