arXiv subjects

Jacob Brown

Publications and source records attributed to Jacob Brown.

10 recordsLinked to original sources

arXiv2026

The Ponderomotive Effects of Narrow-band, Superconducting Resonators in Open and Closed Loop

In this work, we present measurements of ponderomotive instabilities in narrow-band, coaxial resonators in open and closed control loop systems. We show an analytical scheme we will use in future studies to examine the dependency of open loop stability on mechanical parameters. Analytical and simulation models are used to predict the onset of the oscillatory instability in half wave resonators with active disturbance rejection control for amplitude and phase stabilization. We demonstrate that for high amplitude controller bandwidths, ponderomotive oscillations can couple with the controller frequency response via higher harmonics and lower the threshold for the oscillatory instability. We reaffirm the superiority of in-phase/quadrature $(I/Q)$ component control in regards to preventing the oscillatory instability, and show how the phase controller can amplify the crosstalk of disturbances in amplitude and phase control. In cases with large disturbances, this effect can lead to instabilities not present in $I/Q$ control.

physics.acc-ph↗

arXiv2026

Rigorous High-Order Hausdorff Dimension Estimation of Limit Sets of Continued Fraction Iterated Function Systems via B-Splines

We develop a method for the rigorous estimation of Hausdorff dimensions of limit sets produced by continued fraction iterated function systems. Our method is based on the approximation of a Perron-Frobenius operator using the finite element method with B-splines as the choice of basis functions. This choice provides key numerical advantages including higher-order convergence and computational flexibility. We prove an analogue of Falk and Nussbaum's result on "hidden positivity" for B-spline quasi-interpolants to give rigorous upper and lower bounds for the Hausdorff dimensions of various limit sets. We provide numerical results to verify both the rigor and higher-order convergence of our method for quadratic B-spline interpolants in one and two dimensions.

math.NA↗

arXiv2026

Impact of heat treatments on the performance of low-frequency superconducting quarter-wave resonators at 4.3 K

We applied heat treatments to 80.5 MHz quarter-wave resonators made from bulk niobium and prepared with buffered chemical polishing BCP. We evaluated their performance at 4.3 K. We found that a 48 hour, 120 C bake-out ("low-temperature bake out") reduces the surface resistance by a factor of 2 to 3, stemming from a reduction in the Bardeen-Cooper-Schrieffer contribution, consistent with previous findings. This decrease leads to a 38% decrease on average in the medium-field Q-slope when compared to cavities which had only BCP. Mechanisms for the change in quality factor with low-temperature baking have been explored. We observed no improvement in cavity performance after a 3-hour bake-out at 350 C ("medium-temperature bake out"), in contrast to observations for higher-frequency cavities.

physics.acc-ph↗

arXiv2025

A Practical Framework for Simulating Time-Resolved Spectroscopy Based on a Real-time Dyson Expansion

Time-resolved spectroscopy is a powerful tool for probing electron dynamics in molecules and solids, revealing transient phenomena on sub-femtosecond timescales. The interpretation of experimental results is often enhanced by parallel numerical studies, which can provide insight and validation for experimental hypotheses. However, developing a theoretical framework for simulating time-resolved spectra remains a significant challenge. The most suitable approach involves the many-body non-equilibrium Green's function formalism, which accounts for crucial dynamical many-body correlations during time evolution. While these dynamical correlations are essential for observing emergent behavior in time-resolved spectra, they also render the formalism prohibitively expensive for large-scale simulations. Substantial effort has been devoted to reducing this computational cost -- through approximations and numerical techniques -- while preserving the key dynamical correlations. The ultimate goal is to enable first-principles simulations of time-dependent systems ranging from small molecules to large, periodic, multidimensional solids. In this perspective, we outline key challenges in developing practical simulations for time-resolved spectroscopy, with a particular focus on Green's function methodologies. We highlight a recent advancement toward a scalable framework: the real-time Dyson expansion (RT-DE). We introduce the theoretical foundation of RT-DE and discuss strategies for improving scalability, which have already enabled simulations of system sizes beyond the reach of previous fully dynamical approaches. We conclude with an outlook on future directions for extending RT-DE to first-principles studies of dynamically correlated, non-equilibrium systems.

physics.comp-ph↗

arXiv2025

chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data

In this work, we present a mesh-independent, data-driven library, chebgreen, to mathematically model one-dimensional systems, possessing an associated control parameter, and whose governing partial differential equation is unknown. The proposed method learns an Empirical Green's Function for the associated, but hidden, boundary value problem, in the form of a Rational Neural Network from which we subsequently construct a bivariate representation in a Chebyshev basis. We uncover the Green's function, at an unseen control parameter value, by interpolating the left and right singular functions within a suitable library, expressed as points on a manifold of Quasimatrices, while the associated singular values are interpolated with Lagrange polynomials.

cs.LG↗

arXiv2023

Reconfiguration of Amazon's Connectivity in the Climate System

With the recent increase in deforestation, forest fires, and regional temperatures, the concerns around the rapid and complete collapse of the Amazon rainforest ecosystem have heightened. The thresholds of deforestation and the temperature increase required for such a catastrophic event are still uncertain. However, our analysis presented here shows that signatures of changing Amazon are already apparent in historical climate data sets. Here, we extend the methods of climate network analysis and apply them to study the temporal evolution of the connectivity between the Amazon rainforest and the global climate system. We observe that the Amazon rainforest is losing short-range connectivity and gaining more long-range connections, indicating shifts in regional-scale processes. Using embeddings inspired by manifold learning, we show that Amazon connectivity patterns have become more variable in the twenty-first century. By investigating edge-based network metrics on similar regions to the Amazon we see the changing properties of the Amazon are significant in comparison. Furthermore, we simulate diffusion and random walks on these networks and observe a faster spread of perturbations from the Amazon in recent decades. Our methodology innovations can act as a template for examining the spatiotemporal patterns of regional climate change and its impact on global climate using the toolbox of climate network analysis.

physics.ao-ph↗

arXiv2023

Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning

The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probes) for specific destinations. These heuristics, however, are both platform-specific and have been found to be brittle when censors change their blocking behavior, necessitating a more reliable automated process for detecting censorship. In this paper, we explore how machine learning (ML) models can (1) help streamline the detection process, (2) improve the potential of using large-scale datasets for censorship detection, and (3) discover new censorship instances and blocking signatures missed by existing heuristic methods. Our study shows that supervised models, trained using expert-derived labels on instances of known anomalies and possible censorship, can learn the detection heuristics employed by different measurement platforms. More crucially, we find that unsupervised models, trained solely on uncensored instances, can identify new instances and variations of censorship missed by existing heuristics. Moreover, both methods demonstrate the capability to uncover a substantial number of new DNS blocking signatures, i.e., injected fake IP addresses overlooked by existing heuristics. These results are underpinned by an important methodological finding: comparing the outputs of models trained using the same probes but with labels arising from independent processes allows us to more reliably detect cases of censorship in the absence of ground-truth labels of censorship.

cs.LG↗

arXiv2021

Gallium nitride phononic integrated circuits for future RF front-ends

Achieving monolithic integration of passive acoustic wave devices, in particular RF filters, with active devices such as RF amplifiers and switches, is the optimal solution to meet the challenging communication requirements of mobile devices, especially as we move towards the 6G era. This requires a significant ($\approx$100x) reduction in the size of the RF passives, from mm$^2$ footprints in current devices to tens of ${\mu}m^2$ in future systems. Applying ideas from integrated photonics, we demonstrate that high frequency (>3 GHz) sound can be efficiently guided in ${\mu}$m-scale gallium nitride(GaN) waveguides by exploiting the strong velocity contrast available in the GaN on silicon carbide (SiC) platform. Given the established use of GaN devices in RF amplifiers, our work opens up the possibility of building monolithically integrated RF front-ends in GaN-on-SiC.

physics.app-ph↗

arXiv2021

Counting Divisions of a $2\times n$ Rectangular Grid

Consider a $2\times n$ rectangular grid composed of $1\times 1$ squares. Cutting only along the edges between squares, how many ways are there to divide the board into $k$ pieces? Building off the work of Durham and Richmond, who found the closed-form solutions for the number of divisions into 2 and 3 pieces, we prove a recursive relationship that counts the number of divisions of the board into $k$ pieces. Using this recursion, we obtain closed-form solutions for the number of divisions for $k=4$ and $k=5$ using fitting techniques on data generated from the recursion. Furthermore, we show that the closed-form solution for any fixed $k$ must be a polynomial on $n$ with degree $2k-2$.

math.CO↗

arXiv2021

GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security

Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of thousands of devices connected in a complex network topology. The diversity and complexity of these components present an enormous attack surface, allowing an adversary to exploit security vulnerabilities of different devices to execute a potent attack. Though significant efforts have been made to improve the security of individual devices in these systems, little attention has been paid to security at the aggregate level. In this article, we describe a comprehensive risk management system, called GRAVITAS, for IoT/CPS that can identify undiscovered attack vectors and optimize the placement of defenses within the system for optimal performance and cost. While existing risk management systems consider only known attacks, our model employs a machine learning approach to extrapolate undiscovered exploits, enabling us to identify attacks overlooked by manual penetration testing (pen-testing). The model is flexible enough to analyze practically any IoT/CPS and provide the system administrator with a concrete list of suggested defenses that can reduce system vulnerability at optimal cost. GRAVITAS can be employed by governments, companies, and system administrators to design secure IoT/CPS at scale, providing a quantitative measure of security and efficiency in a world where IoT/CPS devices will soon be ubiquitous.

cs.CR↗