Ultracold Neutron Storage Simulation Using the Kassiopeia Software Package

The Kassiopeia software package was originally developed to simulate electromagnetic fields and charged particle trajectories for neutrino mass measurement experiments. Recent additions to Kassiopeia also allow it to simulate neutral particle trajectories in magnetic fields based on their magnetic moments. Two different methods were implemented: an exact method that can work for arbitrary fields and an adiabatic method that is limited to slowly-varying fields but is much faster for large precession frequencies. Additional interactions to simulate reflection of ultracold neutrons from material walls and to allow spin-flip pulses were also added. These tools were used to simulate neutron precession in a room temperature neutron electric dipole moment experiment and predict the values of the longitudinal and transverse relaxation times as well as the trapping lifetime. All three parameters are found to closely match the experimentally determined values when simulated with both the exact and adiabatic methods, confirming that Kassiopeia is able to accurately simulate neutral particles. This opens the door for future uses of Kassiopeia to prototype the next generation of atomic traps and ultracold neutron experiments.

Charged Particle Tracking via Edge-Classifying Interaction Networks

Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high-energy particle physics. In particular, particle tracking data are naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges, given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN’s excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.

A Probabilistic Particle Tracking Framework for Guided and Brownian Motion Systems with High Particle Densities

This paper presents a new framework for particle tracking based on a Gaussian Mixture Model (GMM). It is an extension of the state-of-the-art iterative reconstruction of individual particles by a continuous modeling of the particle trajectories considering the position and velocity as coupled quantities. The proposed approach includes an initialization and a processing step. In the first step, the velocities at the initial points are determined after iterative reconstruction of individual particles of the first four images to be able to generate the tracks between these initial points. From there on, the tracks are extended in the processing step by searching for and including new points obtained from consecutive images based on continuous modeling of the particle trajectories with a Gaussian Mixture Model. The presented tracking procedure allows to extend existing trajectories interactively with low computing effort and to store them in a compact representation using little memory space. To demonstrate the performance and the functionality of this new particle tracking approach, it is successfully applied to a synthetic turbulent pipe flow, to the problem of observing particles corresponding to a Brownian motion (e.g., motion of cells), as well as to problems where the motion is guided by boundary forces, e.g., in the case of particle tracking velocimetry of turbulent Rayleigh–Bénard convection.

Reconfigurable multi-component micromachines driven by optoelectronic tweezers

There is great interest in the development of micromotors which can convert energy to motion in sub-millimeter dimensions. Micromachines take the micromotor concept a step further, comprising complex systems in which multiple components work in concert to effectively realize complex mechanical tasks. Here we introduce light-driven micromotors and micromachines that rely on optoelectronic tweezers (OET). Using a circular micro-gear as a unit component, we demonstrate a range of new functionalities, including a touchless micro-feed-roller that allows the programming of precise three-dimensional particle trajectories, multi-component micro-gear trains that serve as torque- or velocity-amplifiers, and micro-rack-and-pinion systems that serve as microfluidic valves. These sophisticated systems suggest great potential for complex micromachines in the future, for application in microrobotics, micromanipulation, microfluidics, and beyond.

Design of a Hybrid Inertial and Magnetophoretic Microfluidic Device for CTCs Separation from Blood

Circulating tumor cells (CTCs) isolation from a blood sample plays an important role in cancer diagnosis and treatment. Microfluidics offers a great potential for cancer cell separation from the blood. Among the microfluidic-based methods for CTC separation, the inertial method as a passive method and magnetic method as an active method are two efficient well-established methods. Here, we investigated the combination of these two methods to separate CTCs from a blood sample in a single chip. Firstly, numerical simulations were performed to analyze the fluid flow within the proposed channel, and the particle trajectories within the inertial cell separation unit were investigated to determine/predict the particle trajectories within the inertial channel in the presence of fluid dynamic forces. Then, the designed device was fabricated using the soft-lithography technique. Later, the CTCs were conjugated with magnetic nanoparticles and Ep-CAM antibodies to improve the magnetic susceptibility of the cells in the presence of a magnetic field by using neodymium permanent magnets of 0.51 T. A diluted blood sample containing nanoparticle-conjugated CTCs was injected into the device at different flow rates to analyze its performance. It was found that the flow rate of 1000 µL/min resulted in the highest recovery rate and purity of ~95% and ~93% for CTCs, respectively.