Progress Toward a Redetermination of the Neutron Lifetime Through the Absolute Determination of Neutron Flux

The reported lifetime in an in-beam neutron lifetime experiment performed at NIST was tn = (886.3 ± 3.4) s. The largest source of uncertainty was the efficiency of the neutron flux monitor (0.3% relative uncertainty). The flux monitor operates by counting charged particles produced when neutrons impinge on a 6Li foil. Its efficiency was calculated from the 6Li thermal neutron cross section, the solid angle subtended by the charged particle detectors, and the amount of neutron-absorbing material present on the foil. An absolute black neutron detector for cold neutron beams has been developed to measure the efficiency without the need to know these quantities. The flux monitor efficiency is measured to a precision of 0.052% using this direct calibration technique. This calibration removes the largest barrier to a 1 s neutron lifetime measurement with the beam technique. It is hoped that this data can also be used to re-evaluate the current NIST neutron lifetime value, reduce its uncertainty, and remove the dependence on evaluated nuclear data files. There is also the possibility for a direct measurement of the 6Li thermal neutron cross section

Anisotropically Shaped Magnetic/Plasmonic Nanocomposites for Information Encryption and Magnetic-Field-Direction Sensing.

Instantaneous control over the orientation of anisotropically shaped plasmonic nanostructures allows for selective excitation of plasmon modes and enables dynamic tuning of the plasmonic properties. Herein we report the synthesis of rod-shaped magnetic/plasmonic core-shell nanocomposite particles and demonstrate the active tuning of their optical property by manipulating their orientation using an external magnetic field. We further design and construct an IR-photoelectric coupling system, which generates an output voltage depending on the extinction property of the measured nanocomposite sample. We employ the device to demonstrate that the nanocomposite particles can serve as units for information encryption when immobilized in a polymer film and additionally when dispersed in solution can be employed as a new type of magnetic-field-direction sensor

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

Probing the pulsar explanation of the Galactic-Center GeV excess using continuous gravitational-wave searches

Over ten years ago, Fermi observed an excess of GeV gamma rays from the Galactic Center whose origin is still under debate. One explanation for this excess involves annihilating dark matter; another requires an unresolved population of millisecond pulsars concentrated at the Galactic Center. In this work, we use the results from LIGO/Virgo's most recent all-sky search for quasi-monochromatic, persistent gravitational-wave signals from isolated neutron stars, which is estimated to be about 20-50\% of the population, to determine whether unresolved millisecond pulsars could actually explain this excess. First, we choose a luminosity function that determines the number of millisecond pulsars required to explain the observed excess. Then, we consider two models for deformations on millisecond pulsars to determine their ellipticity distributions, which are directly related to their gravitational-wave radiation. Lastly, based on null results from the O3 Frequency-Hough all-sky search for continuous gravitational waves, we find that a large set of the parameter space in the pulsar luminosity function can be excluded. We also evaluate how these exclusion regions may change with respect to various model choices. Our results are the first of their kind and represent a bridge between gamma-ray astrophysics, gravitational-wave astronomy, and dark-matter physics.Comment: Accepted, PRL, 5 pages + appendi

Wire mesh design

We present a computational approach for designing wire meshes, i.e., freeform surfaces composed of woven wires arranged in a regular grid. To facilitate shape exploration, we map material properties of wire meshes to the geometric model of Chebyshev nets. This abstraction is exploited to build an efficient optimization scheme. While the theory of Chebyshev nets suggests a highly constrained design space, we show that allowing controlled deviations from the underlying surface provides a rich shape space for design exploration. Our algorithm balances globally coupled material constraints with aesthetic and geometric design objectives that can be specified by the user in an interactive design session. In addition to sculptural art, wire meshes represent an innovative medium for industrial applications including composite materials and architectural façades. We demonstrate the effectiveness of our approach using a variety of digital and physical prototypes with a level of shape complexity unobtainable using previous methods

A binary self-organizing map and its FPGA implementation

A binary Self Organizing Map (SOM) has been designed and implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of the binary features