arXiv subjects

James D. Turner

Publications and source records attributed to James D. Turner.

4 recordsLinked to original sources

arXiv2026

Searching for optical pulsations from spider millisecond pulsars with the fast photometer SiFAP2

Optical pulsations from millisecond pulsars (MSPs) have recently been discovered from a transitional MSP and an accreting MSP. Evidence for optical pulsations has also been found for a rotation-powered redback MSP. Observations suggest that optical pulsed emission is produced by different mechanisms depending on whether an accretion disk is present in the system or not. We explore a sample of rotation-powered MSPs in close binaries (P$_{orb}<1\,$day, redbacks and black widows), searching for optical periodic signals with the $SiFAP2$ photometer mounted on the $Telescopio\ Nazionale\ Galileo$ (TNG). We perform pulsation searches using the epoch folding method with the most updated (radio or gamma-ray) orbital solution for each system, employing already published solutions or recent observations with Nançay, the Giant Metrewave Radio Telescope, the Parkes Observatory, and $Fermi$-LAT. We update or confirm radio and gamma-ray ephemerides for five of the presented systems. No optical pulsations were detected with a significance exceeding a $3σ$ confidence level in any of our searches, despite often achieving sensitivities on the verge of the instrument's limit. This places correspondingly stringent upper limits on the systems' optical pulsed magnitude and conversion efficiency. Our upper limits are consistent with the pulsed optical luminosity of optical pulsations from the redback PSR J2339-0533. Compared to accreting and transitional MSPs, redback and black widow systems show much lower efficiency in converting their spin down power into pulsed optical emission, supporting the recently advanced idea that the accretion disk around the neutron star plays a role in accelerating the particles responsible for producing bright optical pulsed emission.

astro-ph.HE↗

arXiv2026

An Algebraic Method for Optimizing the State, Control, and Terminal State Weight Matrices for Optimal Feedback Control

The necessary conditions for formulating optimal feedback control algorithms have been known for many years. Free parameters exist in the performance index in the form of state and control penalty and terminal state penalty matrices for tuning the performance of the optimally controlled system. The selection process is typically experimental and iterative. To generate the weight matrices for optimal feedback control algorithms, an optimization process is proposed. Typically, the optimization process for the weight matrices requires several numerical integration processes that are computationally expensive; this work overcomes the classical high computational cost by exploiting closed-form solutions for the time-varying Riccati matrix, state trajectories, state transition matrix, and the optimal performance index. Closed-form algebraic equations are used to generate all partial derivative calculations, and no numerical integration is required. The closed-form partial derivatives are used to generate analytic gradients for the optimization steps. The optimization strategy seeks to minimize the terminal state values for the feedback control problem. A numerical example is presented to demonstrate the effectiveness of the proposed optimization algorithm. The resulting computational procedures are expected to be broadly useful for control theory applications in science and engineering.

math.OC↗

arXiv2022

A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)

Understanding the basins of attraction (BoA) is often a paramount consideration for nonlinear systems. Most existing approaches to determining a high-resolution BoA require prior knowledge of the system's dynamical model (e.g., differential equation or point mapping for continuous systems, cell mapping for discrete systems, etc.), which allows derivation of approximate analytical solutions or parallel computing on a multi-core computer to find the BoA efficiently. However, these methods are typically impractical when the BoA must be determined experimentally or when the system's model is unknown. This paper introduces a model-free sampling method for BoA. The proposed method is based upon hybrid active learning (HAL) and is designed to find and label the "informative" samples, which efficiently determine the boundary of BoA. It consists of three primary parts: 1) additional sampling on trajectories (AST) to maximize the number of samples obtained from each simulation or experiment; 2) an active learning (AL) algorithm to exploit the local boundary of BoA; and 3) a density-based sampling (DBS) method to explore the global boundary of BoA. An example of estimating the BoA for a bistable nonlinear system is presented to show the high efficiency of our HAL sampling method.

cs.LG↗

arXiv2020

Constrained Attractor Selection Using Deep Reinforcement Learning

This paper describes an approach for attractor selection (or multi-stability control) in nonlinear dynamical systems with constrained actuation. Attractor selection is obtained using two different deep reinforcement learning methods: 1) the cross-entropy method (CEM) and 2) the deep deterministic policy gradient (DDPG) method. The framework and algorithms for applying these control methods are presented. Experiments were performed on a Duffing oscillator, as it is a classic nonlinear dynamical system with multiple attractors. Both methods achieve attractor selection under various control constraints. While these methods have nearly identical success rates, the DDPG method has the advantages of a high learning rate, low performance variance, and a smooth control approach. This study demonstrates the ability of two reinforcement learning approaches to achieve constrained attractor selection.

eess.SY↗