arXiv subjects

Jens Weingarten

Publications and source records attributed to Jens Weingarten.

16 recordsLinked to original sources

arXiv2026

Generative Modeling of Complex-Valued Brain MRI Data

Objective. Standard Magnetic Resonance Imaging (MRI) reconstruction pipelines discard phase information captured during acquisition, despite evidence that it encodes tissue properties relevant to tumor diagnosis. Current machine learning approaches inherit this limitation by operating exclusively on reconstructed magnitude images. The aim of this study is to build a generative framework which is capable of jointly modeling magnitude and phase information of complex-valued MRI scans. Approach. The proposed generative framework combines a conditional variational autoencoder, which compresses complex-valued MRI scans into compact latent representations while preserving phase coherence, with a flow-matching-based generative model. Synthetic sample quality is assessed via a real-versus-synthetic classifier and by training downstream classifiers on synthetic data for abnormal tissue detection. Main results. The autoencoder preserves phase coherence above 0.997. Real-versus-synthetic classification yields low AUROC values between 0.50 and 0.66 across all acquisition sequences, indicating generated samples are nearly indistinguishable from real data. In downstream normal-versus-abnormal classification, classifiers trained entirely on synthetic data achieve an AUROC of 0.880, surpassing the real-data baseline of 0.842 on a publicly available dataset (fastMRI). This advantage persists on an independent external test set from a different institution with biopsy-confirmed labels. Significance. The proposed framework demonstrates the feasibility of jointly modeling magnitude and phase information for normal and abnormal complex-valued brain MRI data. Beyond synthetic data generation, it establishes a foundation for the usage of complete brain MRI information in future diagnostic applications and enables systematic investigation of how magnitude and phase jointly encode pathology-specific features.

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arXiv2026

Characterization of Passive CMOS Strip Detectors After Proton Irradiation

Strip detectors are populating outer trackers of high-energy particle experiments. They are convenient for covering large areas of sensitive material since they use less power and have fewer readout channels compared to pixels sensors. Nevertheless, they are typically manufactured with a mask set that covers the full wafer, otherwise when using smaller reticles the strip implants have to be stitched. For this project, strip detectors were fabricated in a CMOS commercial foundry using different reticles to be stitched several times, proving the feasibility of this technology. LFoundry produced the passive CMOS strip detector with a production line of 150 nm node technology, using a 150 um thick FZ wafer. Those strip sensors have three different geometries to study different impacts of the CMOS technology. The strips have lengths of 2.1 cm and 4.1 cm, stitching 3 or 5 reticles respectively. This work shows results of 24 GeV proton irradiated passive CMOS strip detectors. The detectors were irradiated at CERN and were tested with different set-ups, not showing any effect from the strips stitching. Proving that this technology is feasible for detecting high-energy particles opens the door to future large productions of passive strip detectors and also to produce active strip sensors in commercial CMOS foundries.

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arXiv2026

Evaluation of polymer-metal-hybrid bonded wafer-stacks and sensor wafers for ultra-thin hybrid silicon detectors

Semiconductor pixel detectors are widely established in High Energy Physics (HEP) and Medical physics for their high spatial resolution and tracking capabilities. Research on both monolithic detectors and hybrid detectors is ongoing. Monolithic detectors, which integrate the sensor and the read-out electronics in the same die, provide the benefit of reduced thickness but the needed intricate imaging process is only offered by a limited number of chip vendors. The hybrid approach instead facilitates the design and fabrication of sensor and read-out chip using different technologies and opens up access to a large market of semiconductor vendors. For the production of silicon pixel detectors, the interconnection between sensor and read-out chip is usually realized on an individual die level. The needed mechanical stability during the handling of the dies limits their possible thinness. The wafer-to-wafer interconnection process being developed in this project uses a polymer underfill layer between the wafers to provide additional mechanical stability. This allows one to thin the wafer stack significantly after interconnection, bringing the total thickness close to that of monolithic detectors. In this paper, we present first results on the bump bonding yield of the process based on daisy-chain wafer measurements. For the first hybrid pixel detectors produced with this technique, a dedicated sensor wafer was designed and fabricated to be bonded to Timepix3 read-out chip wafers. Results of the characterization of the sensor wafer before hybridization are presented. We show that the wafer-to-wafer bonding process is suitable for hybrid semiconductor pixel detectors.

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arXiv2026

Charge-Carrier transport simulations in diamond detectors with electric-field-dependent mobility and charge-collection-distance-based trapping

Diamond detectors are attractive for operation in harsh radiation environments because they combine radiation tolerance, fast signal formation, and low leakage current. Realistic detector-response simulations require an accurate description of charge-carrier mobility and trapping, which determine both signal amplitude and timing. In this work, we extend \allpix{}, a modular end-to-end detector simulation framework, with diamond-specific transport models. The implementation includes field-dependent mobility parameterizations for electrons and holes and an effective trapping model based on the charge collection distance (CCD), providing a detector-level interface to material quality and radiation-damage measurements. The mobility description is validated in the negligible-trapping limit using single-crystalline CVD diamond by comparing simulated drift velocities and transient-current signals with published reference data. For polycrystalline CVD diamond, the CCD-based trapping model is evaluated using experimentally measured CCD values and compared with laboratory transient-current-technique waveforms. The simulations reproduce the measured drift-velocity behavior in scCVD and the reduced charge collection and degraded transient response observed in pcCVD. The presented implementation enables detector-level studies of charge collection, pulse formation, and timing performance in diamond sensors using experimentally accessible transport and trapping parameters, and provides a practical framework for simulation-driven detector development and radiation-damage studies.

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arXiv2026

Charge-Carrier Mobility in Diamond: Review, Data Compilation, and Modelling for Detector Simulations

Reported electron and hole mobilities, and their saturation velocities, in diamond span orders of magnitude across the literature. We attribute this dispersion primarily to (i) the electric-field window probed in TCT measurements, (ii) the choice of mobility model, and (iii) the excitation source (alpha, laser, or electron). Using an aggregated literature dataset, we benchmark the Trofimenkoff and Caughey-Thomas parameterisations together with a new piecewise model for both conduction- and valence-band transport. For electrons, the piecewise model provides the best global description over a broad electric-field range and is shown to arise as the room-temperature limit of a more general superposition framework that explicitly incorporates intervalley repopulation in the conduction band. For holes, the Caughey-Thomas model remains the statistically preferred description, in line with the absence of a strong repopulation effect in the accessible data. Furthermore, we demonstrate a systematic source dependence (alpha versus laser) and quantify its impact on fitted mobility and saturation-velocity values. We provide temperature scalings over narrow intervals around room temperature and recommend parameter sets for implementation in device and detector simulation frameworks. Together, these results reconcile much of the apparent inconsistency in the literature and offer clear guidance for model selection, experimental design, and device-level simulation of charge transport in intrinsic diamond.

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arXiv2024

TCAD Simulation of Stitching for Passive CMOS Strip Detectors

Most of the tracking detectors for high energy particle experiments are filled with silicon detectors since they are radiation hard, they can give very small spatial resolution and they can take advantage of the silicon electronics foundries developments and production lines. Strip detectors are very useful to cover large areas for tracking purposes, while consuming less power per area compared to pixel sensors. The majority of particle physics experiments use conventional silicon strip detectors fabricated in foundries that do not use stitching, relying on a very small number of foundries worldwide that can provide large amounts of strip detectors. Fabricating strip detectors in a CMOS foundry opens the possibility to use more foundries and to include active elements in the strips for future productions. For the passive CMOS strip detectors project we fabricated strip detectors in a CMOS foundry using two 1 cm2 reticles that are stitched together along the wafer. The fabricated strips stitched the reticles three and five times, and it was shown that the performance of those strips is not affected by the stitching. This paper shows 3D TCAD simulations of the stitching area to investigate the possible effects stitching can have on the performance of the strip detectors, considering different stitching mismatches. We will show that the mismatch of stitched structures up to 1 um does not impact the performance with TCAD simulations which agrees with the results obtained from the measurements.

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arXiv2024

Characterisation and simulation of stitched CMOS strip sensors

In high-energy physics, there is a need to investigate alternative silicon sensor concepts that offer cost-efficient, large-area coverage. Sensors based on CMOS imaging technology present such a silicon sensor concept for tracking detectors. The CMOS Strips project investigates passive CMOS strip sensors fabricated by LFoundry in a 150nm technology. By employing the technique of stitching, two different strip sensor formats have been realised. The sensor performance is characterised based on measurements at the DESY II Test Beam Facility. The sensor response was simulated utilising Monte Carlo methods and electric fields provided by TCAD device simulations. This study shows that employing the stitching technique does not affect the hit detection efficiency. A first look at the electric field within the sensor and its impact on generated charge carriers is being discussed.

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arXiv2023

Accurate and fast deep learning dose prediction for a preclinical microbeam radiation therapy study using low-statistics Monte Carlo simulations

Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumour diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Prescription dose estimations for treating preclinical gliosarcoma models in MRT studies at the Imaging and Medical Beamline at the Australian Synchrotron currently rely on Monte Carlo (MC) simulations. The steep dose gradients associated with the 50$\,μ$m wide coplanar beamlets present a significant challenge for precise MC simulation of the MRT irradiation treatment field in a short time frame. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiation treatment options like MRT. In this work, the successful application of a fast and accurate machine learning dose prediction model in a retrospective preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the gliosarcoma in rodent patients. The predictions of the ML model show excellent agreement with low-noise MC simulations, especially within the investigated tumour volume. This agreement is despite the ML model being deliberately trained with MC-calculated samples exhibiting significantly higher statistical uncertainties. The successful use of high-noise training set data samples, which are much faster to generate, encourages and accelerates the transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.

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arXiv2022

A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets

Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex physical effects such as polarization are therefore automatically taking into account by the model. The macro dose distribution approach described in this study allows for superimposing single microbeam predictions to a beam array field making it an interesting candidate for treatment planning. It is shown that the proposed approach can overcome a main obstacle with microbeam dose predictions by predicting a full microbeam irradiation field in less than a minute while maintaining reasonable accuracy.

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arXiv2022

Characterization of pixelated silicon detectors for daily quality assurance measurements in proton therapy

The advanced imaging and delivery techniques in proton therapy allow conformal high-dose irradiation of the target volume with high accuracy using pencil beam scanning or beam shaping apertures. These irradiation methods increasingly include small radiation fields with large dose gradients, which require detector systems with high spatial resolution for quality assurance. In addition the measurement of all success parameters for daily quality assurance with only one proton field and one simple detector system would save a lot of time in clinical usage. Based on their good spatial resolution and high rate compatibility, pixelated silicon detectors could meet the new requirements. To assess their applicability in proton therapy, ATLAS pixelated silicon detectors are used to measure the lateral beam profile with high spatial resolution. Furthermore, a dose dependent detector calibration is presented to allow the measurement of the requested output constancy. A strategy to verify the proton energy during the daily quality assurance is under study. Promising results from proof-of-principle measurements at the West German Proton Therapy Centre in Essen, Germany, have been obtained.

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arXiv2022

Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D-UNet generative adversarial networks

Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT), require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo physics simulations are recognised to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in pre-clinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 Monte Carlo simulation with adequate accuracy, and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. The energy depositions used for the training of the GAN are obtained using full Geant4 Monte Carlo simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140x18x18 with a voxel edge length of 1 mm. The GAN model consists of two competing 3D convolutional neural networks, which are conditioned on the photon beam and phantom properties. The energy deposition predictions inside all phantom geometries under investigation show deviations of less than 3% of the maximum deposited energy from the simulation for roughly 99% of the voxels in the field of the beam. The computing time for a single prediction is reduced from several hundred hours using Geant4 simulation to less than a second using the GAN model.

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arXiv2021

No more glowing in the dark: How deep learning improves exposure date estimation in thermoluminescence dosimetry

The time- or temperature-resolved detector signal from a thermoluminescence dosimeter can reveal additional information about circumstances of an exposure to ionizing irradiation. We present studies using deep neural networks to estimate the date of a single irradiation with 12 mSv within a monitoring interval of 42 days from glow curves of novel TL-DOS personal dosimeters developed by the Materialprüfungsamt NRW in cooperation with TU Dortmund University. Using a deep convolutional network, the irradiation date can be predicted from raw time-resolved glow curve data with an uncertainty of roughly 1-2 days on a 68% confidence level without the need for a prior transformation into temperature space and a subsequent glow curve deconvolution. This corresponds to a significant improvement in prediction accuracy compared to a prior publication, which yielded a prediction uncertainty of 2-4 days using features obtained from a glow curve deconvolution as input to a neural network.

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arXiv2019

Temperature scaling of reverse current generated in proton irradiated silicon bulk

The value of the scaling parameter $E_{\text{eff}}$ of the temperature dependence for current generated in silicon bulk is investigated for highly irradiated devices. Measurements of devices irradiated to fluences above $1 \times 10^{15} n_{\text{eq}} \text{cm}^{-2} $ have shown a different temperature scaling behaviour than devices irradiated to lower fluences. This paper presents the determination of the parameter $E_{\text{eff}}$ for diodes irradiated with protons up to fluences of $3 \times 10^{15} n_{\text{eq}} \text{cm}^{-2}$ in the bias range from $0$V to $1000$V at temperatures from $-36 ^{\circ}\text{C}$ to $0 ^{\circ}\text{C}$ at different stages of annealing. It is shown that $E_{\text{eff}}$ for highly irradiated devices depends on the applied electric field: below depletion voltage, $E_{\text{eff}}$ is observed to have a lower value than above depletion voltage

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arXiv2018

Beam tests of silicon pixel 3D-sensors developed at SINTEF

For the purpose of withstanding very high radiation doses, silicon pixel sensors with a 3D electrode geometry are being developed. Detectors of this kind are highly interesting for harch radiation environments such as expected in the High Luminosity LHC, but also for space physics and medical applications. In this paper, prototype sensors developed at SINTEF are presented and results from tests in a pion beam at CERN are given. These tests shows that these 3D sensors perform as expected with full efficiency at bias voltages between 5 and 15V.

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arXiv2017

Performance of active edge pixel sensors

To cope with the High Luminosity LHC harsh conditions, the ATLAS inner tracker has to be upgraded to meet requirements in terms of radiation hardness, pile up and geometrical acceptance. The active edge technology allows to reduce the insensitive area at the border of the sensor thanks to an ion etched trench which avoids the crystal damage produced by the standard mechanical dicing process. Thin planar n-on-p pixel sensors with active edge have been designed and produced by LPNHE and FBK foundry. Two detector module prototypes, consisting of pixel sensors connected to FE-I4B readout chips, have been tested with beams at CERN and DESY. In this paper the performance of these modules are reported. In particular the lateral extension of the detection volume, beyond the pixel region, is investigated and the results show high hit-efficiency also at the detector edge, even in presence of guard rings.

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arXiv2005

Validation Studies of the ATLAS Pixel Detector Control System

The ATLAS pixel detector consists of 1744 identical silicon pixel modules arranged in three barrel layers providing coverage for the central region, and three disk layers on either side of the primary interaction point providing coverage of the forward regions. Once deployed into the experiment, the detector will employ optical data transfer, with the requisite powering being provided by a complex system of commercial and custom-made power supplies. However, during normal performance and production tests in the laboratory, only single modules are operated and electrical readout is used. In addition, standard laboratory power supplies are used. In contrast to these normal tests, the data discussed here was obtained from a multi-module assembly which was powered and read out using production items: the optical data path, the final design power supply system using close to final services, and the Detector Control System (DCS). To demonstrate the functionality of the pixel detector system a stepwise transition was made from the normal laboratory readout and power supply systems to the ones foreseen for the experiment, with validation of the data obtained at each transition.

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