arXiv subjects

Thomas King

Publications and source records attributed to Thomas King.

6 recordsLinked to original sources

arXiv2026

Soft X-Ray Detection Down to the Nitrogen K-Edge Using a Pixelated iLGAD Sensor

Hybrid pixel detectors represent the state-of-the-art technology for hard X-ray experiments at synchrotron light sources and free-electron lasers, owing to their high frame rates, extended dynamic range, and scalable detection areas. However, their application in the soft X-ray regime remains bottlenecked by the challenge of developing a sensor that can achieve high quantum efficiency and an sufficient signal-to-noise ratio at low photon energies. Here, we present inverse low-gain avalanche diode (iLGAD) sensors integrated with a thin entrance window, demonstrating single-photon resolution down to 390 eV (below the Nitrogen K-edge) with a signal-to-noise ratio of 5. Two gain layer designs, standard gain layer and shallow gain layer, were systematically investigated to evaluate their spectral response as a function of photon energy and operating temperature. A double-peak feature is observed in the energy spectrum, arising from the dependence of charge multiplication on photon absorption depth. Furthermore, a phenomenological spectral fitting model was established, enabling the quantitative extraction of the effective $\mathrm{n}^+$ layer thickness and gain layer depth. Beyond providing a fundamental understanding of iLGAD charge multiplication mechanisms and characterization results in the soft X-ray regime, these findings lay the foundation for developing large-area, high-frame-rate hybrid pixel detectors for soft X-ray. This advancement directly benefits soft X-ray photon science applications, including high-resolution ptychography, spectroscopy, and resonant scattering at synchrotron and FEL beamlines.

physics.ins-det↗

arXiv2026

Beta decay of potassium-54 reveals the role of two-body currents in neutron-rich nuclei

The beta decay of neutron-rich nuclei probes the microscopic nature of weak interactions in the nuclear medium under extreme conditions. Such weak decays drive the synthesis of heavy elements in astrophysical environments, but remain poorly understood. Far from stability, experimental data are scarce, placing strong demands on ab initio nuclear theory. Two-body weak currents have been proposed as the dominant mechanism responsible for the universal quenching of theoretically predicted Gamow-Teller decay strength. However, direct experimental validation in neutron-rich nuclei has been lacking. Here we measure the beta decay of 54K, a nucleus at the experimental frontier of radioactive ion-beam facilities. In this work, the doubly magic nature of the daughter nucleus 54Ca results in a remarkably simple decay pattern, dominated by a single transition to a neutron-unbound state at 6.2 MeV with a comparative partial half-life logft=4.4(1) or Gamow-Teller decay strength B(GT)=0.26 (+0.08) (-0.04). Our measurement is reproduced by new state-of-the-art calculations of the decay, providing direct evidence for the essential role of two-body currents in nuclear beta decay for systems with a significant neutron excess. We establish a key benchmark for theoretical predictions of weak processes in exotic nuclei.

nucl-ex↗

arXiv2026

Enhanced micrometre spatial resolution and usable photon flux for hybrid pixel detectors via deep-learning-based X-ray localization

Micrometre spatial resolution is achieved for X-rays with the MÖNCH detector, a charge-integrating hybrid pixel detector with a 25 μm pixel pitch, by exploiting charge sharing between neighbouring pixels. The conventional analytical interpolation method, however, relies on a global mapping derived from the charge-sharing statistics and is applicable only to isolated single-photon clusters, which limits both the achievable resolution and the usable photon flux. To overcome these limitations, we trained deep learning models to localize single-photon and pile-up events based on high-fidelity simulation data. For knife-edge measurements at 12 keV and a low occupancy of 0.74% (photons per pixel and frame), where single-photon events dominate, the proposed method reaches a of the edge spread function of 1.36 μm, compared with 1.54 μm for the conventional eta interpolation. We demonstrate experimentally that resolving up to three photons per cluster raises the reconstruction efficiency from 59% to 93% at a 2.4{\times} higher occupancy of 1.81%, while preserving a of 1.39 μm.

physics.ins-det↗

arXiv2025

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI

Understanding how faults nucleate and grow is a critical problem in earthquake science and hazard assessment. This study examines fault development in Alzo granite under triaxial pressures ranging from 5 to 40 MPa by applying a Time Delay Neural Network (TDNN) to multi-parameter acoustic emission (AE) data. The TDNN integrates waveform-derived attributes, including peak delay and scattering attenuation, with occurrence-based metrics such as time distributions, Gutenberg-Richter b-values, and spatial fractal dimensions, to characterize the transition from distributed microcracking to localised faulting. Optimised via genetic algorithms, the TDNN dynamically weights these parameters, enabling accurate characterisation of fault growth stages. Our results delineate three distinct phases of fault evolution: nucleation of random microcracks indicated by changes in elastic wave scattering, initiation of fault growth reflected in evolving AE spatial and magnitude distributions, and fault coalescence marked by exponential increases in peak delay and b-value shifts. The model predicts the timing and magnitude of stress drops across varying pressures and failure mechanisms, from axial splitting to shear localisation, providing deeper insights into fault mechanics through explainable AI models.

physics.geo-ph↗

arXiv2023

Characterization of iLGADs using soft X-rays

Experiments at synchrotron radiation sources and X-ray Free-Electron Lasers in the soft X-ray energy range ($250$eV--$2$keV) stand to benefit from the adaptation of the hybrid silicon detector technology for low energy photons. Inverse Low Gain Avalanche Diode (iLGAD) sensors provide an internal gain, enhancing the signal-to-noise ratio and allowing single photon detection below $1$keV using hybrid detectors. In addition, an optimization of the entrance window of these sensors enhances their quantum efficiency (QE). In this work, the QE and the gain of a batch of different iLGAD diodes with optimized entrance windows were characterized using soft X-rays at the Surface/Interface:Microscopy beamline of the Swiss Light Source synchrotron. Above $250$eV, the QE is larger than $55\%$ for all sensor variations, while the charge collection efficiency is close to $100\%$. The average gain depends on the gain layer design of the iLGADs and increases with photon energy. A fitting procedure is introduced to extract the multiplication factor as a function of the absorption depth of X-ray photons inside the sensors. In particular, the multiplication factors for electron- and hole-triggered avalanches are estimated, corresponding to photon absorption beyond or before the gain layer, respectively.

physics.ins-det↗

arXiv2022

Development of LGAD sensors with a thin entrance window for soft X-ray detection

We show the developments carried out to improve the silicon sensor technology for the detection of soft X-rays with hybrid X-ray detectors. An optimization of the entrance window technology is required to improve the quantum efficiency. The LGAD technology can be used to amplify the signal generated by the X-rays and to increase the signal-to-noise ratio, making single photon resolution in the soft X-ray energy range possible. In this paper, we report first results obtained from an LGAD sensor production with an optimized thin entrance window. Single photon detection of soft X-rays down to 452~eV has been demonstrated from measurements, with a signal-to-noise ratio better than 20.

physics.ins-det↗