arXiv subjects

Kevin Kröninger

Publications and source records attributed to Kevin Kröninger.

At least 19 recordsLinked to original sources

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.

physics.ins-det↗

arXiv2026

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

Reported electron and hole mobilities and saturation velocities in diamond vary widely across the literature. We provide a consolidated review of first-principles predicted and experimentally measured mobility and saturation-velocity values in diamond, alongside a focused assessment of the semi-empirical mobility models used to extract low-field mobilities and high-field saturation velocities. We attribute the dispersion primarily to (i) the electric-field window probed in TCT measurements, (ii) the choice of mobility model, and (iii) the excitation source ($α$, laser, or electron). Using an aggregated literature dataset, we benchmark the Trofimenkoff and Caughey--Thomas parameterizations 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. It can be interpreted 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, consistent with the absence of a repopulation signature in the accessible data. Furthermore, we demonstrate a systematic source dependence ($α$ versus laser) and quantify its impact on fitted mobility and saturation-velocity values. We provide temperature scalings over narrow intervals around room temperature to support Jacoboni--Canali-type parameterization for diamond. Together, these results reconcile much of the apparent inconsistency in the literature and offer guidance for model selection, experimental design, and device-level simulation of charge transport in intrinsic diamond.

physics.ins-det↗

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.

eess.IV↗

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.

physics.ins-det↗

arXiv2026

Efficient Complex-Valued Vision Transformers for MRI Classification Directly from k-Space

Deep learning applications in Magnetic Resonance Imaging (MRI) predominantly operate on reconstructed magnitude images, a process that discards phase information and requires computationally expensive transforms. Standard neural network architectures rely on local operations (convolutions or grid-patches) that are ill-suited for the global, non-local nature of raw frequency-domain (k-Space) data. In this work, we propose a novel complex-valued Vision Transformer (kViT) designed to perform classification directly on k-Space data. To bridge the geometric disconnect between current architectures and MRI physics, we introduce a radial k-Space patching strategy that respects the spectral energy distribution of the frequency-domain. Extensive experiments on the fastMRI and in-house datasets demonstrate that our approach achieves classification performance competitive with state-of-the-art image-domain baselines (ResNet, EfficientNet, ViT). Crucially, kViT exhibits superior robustness to high acceleration factors and offers a paradigm shift in computational efficiency, reducing VRAM consumption during training by up to 68$\times$ compared to standard methods. This establishes a pathway for resource-efficient, direct-from-scanner AI analysis.

cs.CV↗

arXiv2025

Beyond Universality: Probing Lepton Flavor in the SMEFT

We present a global analysis of lepton-flavor-specific operators in the Standard Model Effective Field Theory (SMEFT), combining data from collider and flavor physics experiments. We systematically explore various lepton-flavor scenarios, including flavor-specific, universal, and democratic patterns, while employing a minimal flavor violation (MFV) ansatz in the quark sector. We constrain a set of 17 dimension-six SMEFT operators using a Bayesian fitting approach. Our analysis yields stringent bounds on the Wilson coefficients, probing new physics scales up to $\mathcal{O}(1000)$\,TeV. The strongest constraints are obtained in lepton-flavor universal and lepton-flavor democratic scenarios, in particular for the electroweak dipole operators. We further provide posterior-based predictions for $B \to K^{(*)} ν\bar ν$ and $B \to (π, ρ) ν\bar ν$ decays, highlighting their role as probes of the MFV hypothesis in upcoming Belle II measurements. Our results demonstrate the complementarity of collider and flavor observables, the impact of flavor assumptions on global SMEFT fits, and establish robust limits on extensions of the Standard Model in the lepton sector.

hep-ph↗

arXiv2025

Road map for the tuning of hadronic interaction models with accelerator-based and astroparticle data

In high-energy and astroparticle physics, event generators play an essential role, even in the simplest data analyses. As analysis techniques become more sophisticated, e.g. based on deep neural networks, their correct description of the observed event characteristics becomes even more important. Physical processes occurring in hadronic collisions are simulated within a Monte Carlo framework. A major challenge is the modeling of hadron dynamics at low momentum transfer, which includes the initial and final phases of every hadronic collision. QCD-inspired phenomenological models used for these phases cannot guarantee completeness or correctness over the full phase space. These models usually include parameters which must be tuned to suitable experimental data. Until now, event generators have been developed and tuned mainly on the basis of data from high-energy physics experiments at accelerators. The wealth of data available from the latest generation of astroparticle experiments has not yet been fully exploited, and in many cases is not satisfactorily described. Both kinds of data sets are complementary as astroparticle experiments provide sensitivity especially to hadrons produced nearly parallel to the collision axis and cover center-of-mass energies up to several hundred TeV, well beyond those reached at colliders so far. In this report, we provide an overview of state-of-the-art event generators and their tuning, including the most relevant inputs from high-energy accelerator and astroparticle experiments. We present a road map that shows, for the first time, how the unified tuning of event generators with accelerator-based and astroparticle data can be performed.

astro-ph.HE↗

arXiv2025

Improving Air Shower Simulations by Tuning Pythia 8/Angantyr with Accelerator Data

We present a combined analysis of the Pythia 8 event generator using accelerator data and evaluate its impact on air shower observables. Reliable simulations with event generators are essential for particle physics analyses, achievable through advanced tuning to experimental data. Pythia 8 has emerged as a promising high-energy interaction model for cosmic ray air shower simulations, offering well-documented parameter settings and a user-friendly interface to enable automatic tuning efforts. Using data from collider and fixed-target experiments, we first derive tunes for each domain separately, before tuning both domains simultaneously. To achieve this, we define a core set of observables and quantify their dependence on selected parameters. The tuning efforts are based on gradient descent and Bayesian methods, the latter providing a full uncertainty propagation of the parameters to the observables. Results for the impact of a combined analysis for the Pythia 8/Angantyr event generator on air shower observables, such as particle densities at ground level and energy deposit profiles, are presented.

astro-ph.HE↗

arXiv2025

Prompt and Conventional High-Energy Muon Spectra from a full Monte Carlo Simulation via $\texttt{CORSIKA7}$

Extensive air showers produce high-energy muons that can be utilized to probe hadronic interaction models in cosmic ray interactions. Most muons originate from pion and kaon decays, called $\textit{conventional}$ muons, while a smaller fraction, referred to as $\textit{prompt}$ muons, arises from the decay of heavier, short-lived hadrons. The $\texttt{EHISTORY}$ option of the air shower simulation tool $\texttt{CORSIKA7}$ is used in this work to investigate the prompt and conventional muon flux in the energy range of 100 TeV to 100 PeV, utilizing the newly developed open-source python software $\texttt{PANAMA}$. Identifying the muon parent particles allows for scaling the contribution of prompt particles, which can be leveraged by future experimental analyses to measure the normalization of the prompt muon flux. Obtained prompt muon spectra from $\texttt{CORSIKA7}$ are compared to $\texttt{MCEq}$ results. The relevance to large-volume neutrino detectors, such as IceCube and KM3NeT, and the connection to hadronic interaction models is discussed.

astro-ph.HE↗

arXiv2025

PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation

Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magnitude images, discarding the phase data despite its potential for downstream tasks, such as tumor segmentation and classification. In this work, we introduce $\textit{PhaseGen}$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice. This enables the creation of artificial complex-valued raw data, allowing pretraining for models that require k-Space information. We evaluate PhaseGen on two tasks: skull-stripping directly in k-Space and MRI reconstruction using the publicly available FastMRI dataset. Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data, with an increased segmentation accuracy from $41.1\%$ to $80.1\%$, and enhances MRI reconstruction when combined with limited real-world data. This work presents a step forward in utilizing generative AI to bridge the gap between magnitude-based datasets and the complex-valued nature of MRI raw data. This approach allows researchers to leverage the vast amount of avaliable image domain data in combination with the information-rich k-Space data for more accurate and efficient diagnostic tasks. We make our code publicly $\href{https://github.com/TIO-IKIM/PhaseGen}{\text{available here}}$.

eess.IV↗

arXiv2025

Measuring $A_\text{FB}^b$ and $R_b$ with exclusive $b$-hadron decays at the FCC-ee

This paper presents a novel tagging technique to measure the beauty-quark partial decay-width ratio $R_b$ and its forward-backward asymmetry $A_\text{FB}^b$ at the FCC-ee, using $\mathcal{O}(10^{12})$ $Z$-boson decays. The method is based on the exclusive reconstruction of a selected list of $b$-hadron decay modes in $Z\to b\bar{b}$ events at the $Z$ pole, which can provide the flavour and possibly the charge of the hemisphere. This approach effectively eliminates the contamination from light-quark physics events and reduces the leading systematic uncertainties arising from background contamination, tagging-efficiency correlations, and gluon-radiation corrections by exploiting the geometric and kinematic properties of beauty hadrons. This results in a total relative uncertainty of the order of $0.01\,\%$ for both observables. Furthermore, this precision allows to obtain a commensurate precision on the weak mixing angle $\sin^2(θ_W^\text{eff})$ compared to the muon forward-backward asymmetry on the order of $0.002\,\%$.

hep-ex↗

arXiv2025

MCBench: A Benchmark Suite for Monte Carlo Sampling Algorithms

In this paper, we present MCBench, a benchmark suite designed to assess the quality of Monte Carlo (MC) samples. The benchmark suite enables quantitative comparisons of samples by applying different metrics, including basic statistical metrics as well as more complex measures, in particular the sliced Wasserstein distance and the maximum mean discrepancy. We apply these metrics to point clouds of both independent and identically distributed (IID) samples and correlated samples generated by MC techniques, such as Markov Chain Monte Carlo or Nested Sampling. Through repeated comparisons, we evaluate test statistics of the metrics, allowing to evaluate the quality of the MC sampling algorithms. Our benchmark suite offers a variety of target functions with different complexities and dimensionalities, providing a versatile platform for testing the capabilities of sampling algorithms. Implemented as a Julia package, MCBench enables users to easily select test cases and metrics from the provided collections, which can be extended as needed. Users can run external sampling algorithms of their choice on these test functions and input the resulting samples to obtain detailed metrics that quantify the quality of their samples compared to the IID samples generated by our package. This approach yields clear, quantitative measures of sampling quality and allows for informed decisions about the effectiveness of different sampling methods. By offering such a standardized method for evaluating MC sampling quality, our benchmark suite provides researchers and practitioners from many scientific fields, such as the natural sciences, engineering, or the social sciences with a valuable tool for developing, validating and refining sampling algorithms.

stat.CO↗

arXiv2024

Phase space sampling with Markov Chain Monte Carlo methods

We present a study on using Markov Chain Monte Carlo (MCMC) techniques to explore the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders. To this end, we combine the BAT.jl package that provides implementations of a variety of MCMC algorithms with the Sherpa event generator framework. We discuss technical aspects of the implementation and the resulting algorithm and present first results for the process of $Z+3$ jets production at the LHC.

hep-ph↗

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.

physics.ins-det↗

arXiv2023

More Synergies from Beauty, Top, $Z$ and Drell-Yan Measurements in SMEFT

We perform a global analysis of Beauty, Top, $Z$ and Drell-Yan measurements in the framework of the Standard Model effective theory (SMEFT). We work within the minimal flavor violation (MFV) hypothesis, which relates different sectors and generations beyond the $SU(2)_L$-link between left-handed top and beauty quarks. We find that the constraints on the SMEFT Wilson coefficients from the combined analysis are stronger than the constraints from a fit to the individual sectors, highlighting synergies in the global approach. We also show that constraints within MFV are strengthened compared to single-generation fits. The strongest bounds are obtained for the semileptonic four-fermion triplet operator $C_{lq}^{(3)}$, probing scales as high as $18$ TeV, followed by the gluon dipole operator $C_{uG}$ with $7$ TeV, and other four-fermion and penguin operators in the multi-TeV range. Operators with left-handed quark bilinears receive order one contributions from higher orders in the MFV expansion induced by the top Yukawa coupling as a result of the FCNC $b \to s μμ$ anomalies combined with the other sectors. We predict the $68\%$ credible intervals of the dineutrino branching ratios within MFV as $5.3 \cdot 10^{-6} \leq {\cal{B}}(B^0 \to K^{* 0} ν\barν) \leq 12.8 \cdot 10^{-6}$ and $ 2.5 \cdot 10^{-6} \leq {\cal{B}}(B^+ \to K^+ ν\barν) \leq 5.9 \cdot 10^{-6}$, which include the respective Standard Model predictions, and are in reach of the Belle II experiment. We show how future measurements of the dineutrino branching ratios can provide insights into the structure of new physics in the global fit.

hep-ph↗

arXiv2023

A Bayesian tune of the Herwig Monte Carlo event generator

The optimisation (tuning) of the free parameters of Monte Carlo event generators by comparing their predictions with data is important since the simulations are used to calculate experimental efficiency and acceptance corrections, or provide predictions for signatures of hypothetical new processes in experiments. We present a tuning procedure that is based on Bayesian reasoning and that allows for a proper statistical interpretation of the results. The parameter space is fully explored using Markov Chain Monte Carlo. We apply the tuning procedure to the Herwig7 event generator with both the cluster and the string hadronization models and a large set of measurements from hadronic Z-boson decays produced at LEP in $e^{+}e^{-}$ collisions. Furthermore, we introduce a coherent propagation of uncertainties from the realm of parameters to the realm of observables and we show the effects of including experimental correlations of the measurements. To allow comparison with the approaches of other groups, we repeat the tuning considering weights for individual measurements.

hep-ph↗

arXiv2023

Synergies of Drell-Yan, beauty, top, and Z observables in MFV-SMEFT

The Standard Model Effective Field Theory (SMEFT) is a powerful tool to search for new physics in a model-independent way. We explore the synergies arising from different types of observables in a combined, global SMEFT fit. Specifically, we investigate the combination of top-quark measurements, $b\to s$ flavor changing neutral current transitions, $Z\to b \bar b$ and $Z\to c \bar c$, as well as Drell-Yan data from the LHC. We also examine the impact of Minimal Flavor Violation (MFV) as a flavor pattern in the global fit. We find that the combination of high-p$_T$ with flavor physics observables provides powerful synergies that significantly improve the fit and enable more precise tests of various SMEFT operators. By incorporating different observables, we are able to remove flat directions in the parameter space and infer on the flavor structure based on the MFV parameterization. In particular, we find that MFV significantly strengthens the constraints in comparison to a flavor-specific approach. Furthermore, our analysis yields a prediction for the dineutrino branching ratios ${\cal{B}}(B \to K^{(*)} ν\bar ν)$ within MFV, which can be tested experimentally at Belle II.

hep-ph↗

arXiv2023

k-strip: A novel segmentation algorithm in k-space for the application of skull stripping

Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work directly with the complex raw k-space data. Skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain were used as the ground truth. Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above the eye-region reach DICE scores of up to 99\%, while the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-strip often smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. Conclusion: With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.

eess.IV↗