arXiv subjects

Kevin Kröninger

Publications and source records attributed to Kevin Kröninger.

At least 37 records · Page 2Linked to original sources

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.

physics.med-ph↗

arXiv2022

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

hep-ph↗

arXiv2022

Synergies of Drell-Yan, top and beauty in global SMEFT fits

We present a global fit of Drell-Yan, top-quark and beauty-physics data in the Standard Model Effective Field Theory framework using the Minimal Flavor Violation ansatz for the quark-flavor structure. The different energy scales are thereby connected by applying the renormalization group equation and matching onto the Weak Effective Theory in order to incorporate $b\to s$ FCNCs at low energies into the fit. We find that the combination of the different sectors greatly improves the bounds on the Wilson coefficients.

hep-ph↗

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.

physics.med-ph↗

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.

physics.med-ph↗

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.

physics.med-ph↗

arXiv2021

Reformulation of a likelihood approach to fake-lepton estimation in the framework of Bayesian inference

Prompt isolated leptons are essential in many analyses in high-energy particle physics but are subject to fake-lepton background, i.e. objects that mimic the lepton signature. The fake-lepton background is difficult to estimate from simulation and is often directly determined from data. A popular method is the matrix method, which however suffers from several limitations. This paper recapitulates an alternative approach based on a likelihood with Poisson constraints and reformulates the problem from a different starting point in the framework of Bayesian statistics. The equality of both approaches is shown and several cases are studied in which the matrix method is limited. In addition, the fake lepton background is recalculated and compared to the estimate with the matrix method in an example top-quark measurement.

hep-ph↗

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.

physics.med-ph↗

arXiv2021

Top and Beauty synergies in SMEFT-fits at present and future colliders

We perform global fits within Standard Model Effective Field Theory (SMEFT) combining top-quark pair production processes and decay with $b\rightarrow s$ flavor changing neutral current transitions and $Z \to b \bar b$ in three stages: using existing data from the LHC and $B$-factories, using projections for the HL-LHC and Belle II, and studying the additional new physics impact from a future lepton collider. The latter is ideally suited to directly probe $\ell^+\ell^-\rightarrow t\bar t$ transitions. We observe powerful synergies in combining both top and beauty observables as flat directions are removed and more operators can be probed. We find that a future lepton collider significantly enhances this interplay and qualitatively improves global SMEFT fits.

hep-ph↗

arXiv2021

Synergies of top and $B$ anomalies in SMEFT

We present constraints on Wilson coefficients from a combined fit to data from top-quark and beauty-physics measurements within the Standard Model effective field theory. We consider present data on top-quark pair production and decay, $b\to s$ flavor changing neutral currents, and $Z\to b\bar b$ decay, as well as different future scenarios. These comprise projections for HL-LHC, Belle II, and a lepton collider using the example of CLIC. We investigate opportunities for detecting deviations from the Standard Model hinted at by present data on $b\to s$ transitions. In the fit, we find strong synergies between top-quark and beauty physics that allow to tighten constraints on various coefficients.

hep-ph↗

arXiv2020

Correlating uncertainties in global analyses within SMEFT matters

We investigate the impact of correlations between (theoretical and experimental) uncertainties on multi-experiment, multi-observable analyses within the Standard Model Effective Field Theory (SMEFT). To do so, we perform a model-independent analysis of $t$-channel single top-quark production and top-quark decay data from ATLAS, CMS, CDF and D0. We show quantitatively how the fit changes when different experimental or theoretical correlations are assumed. Scaling down statistical uncertainties according to the luminosities of future colliders with $300 \, {\rm fb}^{-1}$ and higher, we find that this effect becomes a matter of life and death: assuming no correlations returns a fit in agreement with the Standard Model while a 'best guess'-ansatz taking into account correlations would observe new physics. At the same time, modelling the impact of higher order SMEFT-corrections the latter turn out to be a subleading source of uncertainty only.

hep-ph↗

arXiv2020

BAT.jl -- A Julia-based tool for Bayesian inference

We describe the development of a multi-purpose software for Bayesian statistical inference, BAT.jl, written in the Julia language. The major design considerations and implemented algorithms are summarized here, together with a test suite that ensures the proper functioning of the algorithms. We also give an extended example from the realm of physics that demonstrates the functionalities of BAT.jl.

stat.CO↗

arXiv2020

Study of interference effects in the search for flavour-changing neutral current interactions involving the top quark and a photon or a $Z$ boson at the LHC

Flavour-changing neutral-current interactions of the top quark can be searched for in top-quark pair production with one top quark decaying to an up-type quark and a neutral boson, and they can be searched for in the single production of a top quark in association with such a boson. Both processes interfere if an additional up-type quark is produced in the case of single production. The impact of these interference effects on searches for flavour-changing neutral currents at the LHC is studied for the case where the neutral boson is a photon or a $Z$ boson. Interference effects are found to be smaller than variations of the renormalisation and factorisation scales.

hep-ph↗

arXiv2020

Constraining top-quark couplings combining top-quark and $\boldsymbol{B}$ decay observables

We present a first, consistent combination of measurements from top-quark and $B$ physics to constrain top-quark properties within the Standard Model Effective Field Theory (SMEFT). We demonstrate the feasibility and benefits of this approach and detail the ingredients required for a proper combination of observables from different energy scales. Specifically, we employ measurements of the $t\bar tγ$ cross section together with measurements of the $\bar B\rightarrow X_sγ$ branching fraction to test the Standard Model and look for new physics contributions to the couplings of the top quark to the gauge bosons within SMEFT. We perform fits of three Wilson coefficients of dimension-six operators considering only the individual observables as well as their combination to demonstrate how the complementarity between top-quark and $B$ physics observables allows to resolve ambiguities and significantly improves the constraints on the Wilson coefficients. No significant deviation from the Standard Model is found with present data.

hep-ph↗

arXiv2019

Quenching of $g_{\rm A}$ deduced from the $β$-spectrum shape of $^{113}$Cd measured with the COBRA experiment

A dedicated study of the quenching of the weak axial-vector coupling strength $g_{\rm A}$ in nuclear processes has been performed by the COBRA collaboration. This investigation is driven by nuclear model calculations which show that the $β$-spectrum shape of the fourfold forbidden non-unique decay of $^{113}$Cd strongly depends on the effective value of $g_{\rm A}$. Using an array of CdZnTe semiconductor detectors, 45 independent $^{113}$Cd spectra were obtained and interpreted in the context of three nuclear models. The resulting effective mean values are $\bar{g}_{\rm A}(\text{ISM}) = 0.915 \pm 0.007$, $\bar{g}_{\rm A}(\text{MQPM}) = 0.911 \pm 0.013$ and $\bar{g}_{\rm A}(\text{IBFM-2}) = 0.955 \pm 0.022$. These values agree well within the determined uncertainties and deviate significantly from the free value of $g_{\rm A}$. This can be seen as a first step towards answering the long-standing question regarding quenching effects related to $g_{\rm A}$ in low-energy nuclear processes.

nucl-ex↗

arXiv2019

From the Bottom to the Top -- Reconstruction of $t\bar{t}$ Events with Deep Learning

The reconstruction of top-quark pair-production ($t\bar{t}$) events is a prerequisite for many top-quark measurements. We use a deep neural network, trained with Monte-Carlo simulated events, to reconstruct $t\bar{t}$ decays in the lepton+jets final state. Comparing our approach to a widely-used kinematic fit, we find significant improvements in the correct assignment of jets to the partons from the decay, and we study the reconstruction performance of several kinematic top-quark properties. We document our workflow for the optimisation of the hyperparameters of the deep neural network. This workflow can be followed by experimental collaborations to retrain the network taking into account their detailed detector simulations.

hep-ex↗

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

physics.ins-det↗

arXiv2017

Planar n-in-n quad module prototypes for the ATLAS ITk upgrade at HL-LHC

In order to meet the requirements of the High Luminosity LHC (HL-LHC), it will be necessary to replace the current tracker of the ATLAS experiment. Therefore, a new all-silicon tracking detector is being developed, the so-called Inner Tracker (ITk). The use of quad chip modules is intended in its pixel region. These modules consist of a silicon sensor that forms a unit along with four read-out chips. The current ATLAS pixel detector consists of planar n-in-n silicon pixel sensors. Similar sensors and four FE-I4 read-out chips were assembled to first prototypes of planar n-in-n quad modules. The main focus of the investigation of these modules was the region between the read-out chips, especially the central area between all four read-out chips. There are special pixel cells placed on the sensor which cover the gap between the read-out chips. This contribution focuses on the characterization of a non-irradiated device, including important sensor characteristics, charge collection determined with radioactive sources as well as hit efficiency measurements, performed in the laboratory and at testbeams. In addition, first laboratory results of an irradiated device are presented.

physics.ins-det↗