arXiv subjects

T. King

Publications and source records attributed to T. King.

6 recordsLinked to original sources

arXiv2025

Improved Pixel-wise Calibration for Charge-Integrating Hybrid Pixel Detectors with Performance Validation

The M\"ONCH hybrid pixel detector, with a 25 \textmu m pixel pitch and fast charge-integrating readout, has demonstrated subpixel resolution capabilities for X-ray imaging and deep learning-based electron localization in electron microscopy. Fully exploiting this potential requires extensive calibration to ensure both linearity and uniformity of the pixel response, which is challenging for detectors with a large dynamic range. To overcome the limitations of conventional calibration methods, we developed an accurate and efficient correction method to achieve pixel-wise gain and nonlinearity calibration based on the backside pulsing technique. A three-dimensional lookup table was generated for all pixels across the full dynamic range, mapping the pixel response to a calibrated linear energy scale. Compared with conventional linear calibration, the proposed method yields negligible deviations between the calibrated and nominal energies for photons and electrons. The improvement in energy resolution ranges from 4% to 22% for 15-25 keV photons and from 16% to 23% for 60-200 keV electrons. Deep learning-based electron localization demonstrates a 4% improvement in spatial resolution when using the proposed calibration method. This approach further enables rapid diagnosis of the cause of bad pixels and estimation of bump-bonding yield.

physics.ins-det↗

arXiv2025

Optimization and validation of charge transport simulation for hybrid pixel detectors incorporating the repulsion effect

For emerging applications of hybrid pixel detectors which require high spatial resolution, e.g., subpixel interpolation in X-ray imaging and deep learning-based electron localization, accurate modeling of charge transport processes in the sensor is highly demanded. To address this, two open-source, time-stepping Monte Carlo simulation methods have been developed, both explicitly incorporating charge repulsion, which are found necessary for accurate simulation when charge sharing becomes important. The first method employs brute-force calculations accelerated by GPU computing to model charge carrier dynamics, including drift, diffusion, and repulsion. The second utilizes a simplified spherical model that significantly reduces computational complexity. A parameterization scheme of the charge transport behaviors has been developed to enable efficient and rapid generation of X-ray simulation events. Both methods were rigorously validated using experimental data collected with a monochromatic X-ray beam at the METROLOGIE beamline of the SOLEIL synchrotron, demonstrating excellent agreement with measured pixel-energy spectra across various sensor thicknesses, bias voltages, and photon energies. Furthermore, the impact of the repulsion effect on charge carrier distributions was quantitatively evaluated. The potential applications of these simulation methods for different particle detections and detector technologies are also discussed.

physics.ins-det↗

arXiv2025

Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

physics.ins-det↗

arXiv2024

Optimizing Charge Transport Simulation for Hybrid Pixel Detectors

To enhance the spatial resolution of the M\"ONCH 25 \textmu m pitch hybrid pixel detector, deep learning models have been trained using both simulation and measurement data. Challenges arise when comparing simulation-based deep learning models to measurement-based models for electrons, as the spatial resolution achieved through simulations is notably inferior to that from measurements. Discrepancies are also observed when directly comparing X-ray simulations with measurements, particularly in the spectral output of single pixels. These observations collectively suggest that current simulations require optimization. To address this, the dynamics of charge carriers within the silicon sensor have been studied using Monte Carlo simulations, aiming to refine the charge transport modeling. The simulation encompasses the initial generation of the charge cloud, charge cloud drift, charge diffusion and repulsion, and electronic noise. The simulation results were validated with measurements from the M\"ONCH detector for X-rays, and the agreement between measurements and simulations was significantly improved by accounting for the charge repulsion.

physics.ins-det↗

arXiv2023

New isomeric transition in $^{36}$Mg: Bridging the N=20 and N=28 islands of inversion

We observed a new isomeric gamma transition at 168 keV in $^{36}$Mg, with a half-life of T$_{1/2}$=[130-500]$(\pm40)(^{+800}_{-20})_{sys}$ ns. We propose that the observed transition de-excites a new 0$^+$ isomeric state and populates the previously known first 2$^+$ state. The existence of this isomer is consistent with the predictions of the large-scale shell model calculations of $^{36}$Mg using the sdpf-u-mix interaction. The observed excitation energy of the second 0$^+$ state is caused by the small energy separation between two prolate-deformed configurations where the intruder configuration corresponds to two neutron excitations from the {\it sd} to the {\it pf} shell. Within this interpretation, $^{36}$Mg becomes the crossing point between nuclei in which ground state deformed/superdeformed configurations are caused by the dominance of N=20 intruders ($^{32,34}$Mg) and nuclei where deformed configurations are associated with N=28 intruders ($^{38}$Mg and beyond). We found the lack of three-body monopole corrections in other effective interactions results in a predominance of N=20 intruder configurations past $^{38}$Mg incompatible with our observation. We conclude that $^{36}$Mg bridges the N=20 and N=28 islands of inversion, forming the so-called Big Island of Deformation.

nucl-ex↗

arXiv2018

Commissioning of the BRIKEN detector for the measurement of very exotic beta-delayed neutron emitters

A new detection system has been installed at the RIKEN Nishina Center (Japan) to investigate decay properties of very neutron-rich nuclei. The setup consists of three main parts: a moderated neutron counter, a detection system sensitive to the implantation and decay of radioactive ions, and gamma-ray detectors. We describe here the setup, the commissioning experiment and some selected results demonstrating its performance for the measurement of half-lives and beta-delayed neutron emission probabilities. The methodology followed in the analysis of the data is described in detail. Particular emphasis is placed on the correction of the accidental neutron background.

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