arXiv subjects

Matthew Britton

Publications and source records attributed to Matthew Britton.

4 recordsLinked to original sources

arXiv2026

Advanced Control of Electron Beams: Tailoring X-ray Production with Programmable Laser Shaping

Leveraging the full scientific capabilities of next-generation high-repetition-rate free-electron lasers requires programmable control over electron-beam properties at their source. The photoinjector drive laser defines the electron beam's initial six-dimensional phase-space distribution, yet has historically been limited to Gaussian or static flat-top profiles, with most manipulation occurring downstream. Here we demonstrate software-programmable ultraviolet pulse shaping at the LCLS-II photoinjector as a source-level actuator that complements traditional accelerator controls. Using a coupled architecture combining dispersion-controlled nonlinear frequency conversion with spatial-light-modulator spectral shaping, we generate user-defined temporal structures and observe their imprint on electron bunches through high-resolution time-domain diagnostics. Laser-imposed multi-peaked modulation persists through acceleration, magnetic compression, and undulator transport with shot-to-shot repeatability, producing clearly resolved current structure in the compressed beam. Variance-based reconstruction from transverse deflecting cavity measurements reveals structured X-ray emission profiles exhibiting temporal features consistent with the programmed laser waveform. By providing rapid, software-controlled reconfiguration of electron-beam initial conditions, this source-level control approach establishes a programmable upstream actuator for future adaptive optimization and autonomous facility operation at high-repetition-rate light sources.

physics.acc-ph↗

arXiv2026

Refactoring and Equivalence in Rust: Expanding the REM Toolchain with a Novel Approach to Automated Equivalence Proofs

Refactoring tools are central to modern development, with extract-function refactorings used heavily in day-to-day work. For Rust, however, ownership, borrowing, and advanced type features make automated extract-function refactoring challenging. Existing tools either rely on slow compiler-based analysis, support only restricted language fragments, or provide little assurance beyond "it still compiles." This paper presents REM2.0, a new extract-function and verification toolchain for Rust. REM2.0 works atop rust-analyzer as a persistent daemon, providing low-latency refactorings with a VSCode front-end. It adds a repairer that automatically adjusts lifetimes and signatures when extraction exposes borrow-checker issues, and an optional verification pipeline connecting to CHARON and AENEAS to generate Coq equivalence proofs for a supported Rust subset. The architecture is evaluated on three benchmark suites. On the original REM artefact, REM2.0 achieves 100% compatibility while reducing latency from ~1000ms to single-digit milliseconds in the daemon. On 40 feature-focused extractions from 20 highly starred GitHub repositories, REM2.0 handles most examples involving async/await, const fn, non-local control flow, generics, and higher-ranked trait bounds. On twenty verification benchmarks, the CHARON/AENEAS pipeline constructs end-to-end equivalence proofs for cases within its current subset. Overall, results show that a rust-analyzer-based design can provide fast, feature-rich extract-function refactoring for real Rust programs, while opt-in verification delivers machine-checked behaviour preservation.

cs.PL↗

arXiv2019

VINE: Visualizing Statistical Interactions in Black Box Models

As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional explanations - how groups of similar instances behave in a complex model, and the related issue of visualizing statistical feature interactions. The lack of utilities available for these analytical needs hinders the development of models that are mission-critical, transparent, and align with social goals. We present VINE (Visual INteraction Effects), a novel algorithm to extract and visualize statistical interaction effects in black box models. We also present a novel evaluation metric for visualizations in the interpretable ML space.

cs.LG↗

arXiv2006

The Anisoplanatic Point Spread Function in Adaptive Optics

The effects of anisoplanatism on the adaptive optics point spread function are investigated. A model is derived that combines observations of the guide star with an analytic formulation of anisoplanatism to generate predictions for the adaptive optics point spread function at arbitrary locations within the field of view. The analytic formulation captures the dependencies of anisoplanatism on aperture diameter, observing wavelength, angular offset, zenith angle and turbulence profile. The predictions of this model are compared to narrowband 2.12 um and 1.65 um images of a 21 arcsec binary (mV=7.3, 7.6) acquired with the Palomar Adaptive Optics System on the Hale 5 meter telescope. Contemporaneous measurements of the turbulence profile made with a DIMM/MASS unit are used together with images of the primary to predict the point spread function of the binary companion. Predicted companion Strehl ratios are shown to match measurements to within a few percent, whereas predictions based on the isoplanatic angle approximation are highly discrepant. The predicted companion point spread functions are shown to agree with observations to 10%. These predictions are used to measure the differential photometry between binary members to an accuracy of 1 part in 10^{3}, and the differential astrometry to an accuracy of 1 mas. Errors in the differential astrometry are shown to be dominated by differential atmospheric tilt jitter. These results are compared to other techniques that have been employed for photometry, astrometry, and high contrast imaging.

astro-ph↗