arXiv subjects

Anders Persson

Publications and source records attributed to Anders Persson.

8 recordsLinked to original sources

arXiv2026

Enhanced dynamic range spatio-spectral metrology of few-cycle laser pulses

Accurate spatio-temporal and spatio-spectral metrology is critical to the characterization and use of ultra-short, high-power lasers. The emergence of few cycle pulses, with bandwidths of tens or hundreds of nanometers, poses a significant challenge to existing metrology techniques. This is due both to large discrepancies in the sensitivities of the measurements at different wavelengths and to variation in the spectral intensity at those wavelengths. In this paper, the authors propose spectral filtering and stitching of the measurements as a robust, simple solution that enhances the dynamic range of the measurements, allowing accurate few-cycle pulse reconstruction. This enhancement is demonstrated using INSIGHT -- the most commonly used spatio-spectral measurement device -- as well as using IMPALA and spatially resolved Fourier transform spectrometry.

physics.optics↗

arXiv2025

Topology-Aware Volume Fusion for Spectral Computed Tomography via Histograms and Extremum Graph

Photon-Counting Computed Tomography (PCCT) is a novel imaging modality that simultaneously acquires volumetric data at multiple X-ray energy levels, generating separate volumes that capture energy-dependent attenuation properties. Attenuation refers to the reduction in X-ray intensity as it passes through different tissues or materials. This spectral information enhances tissue and material differentiation, enabling more accurate diagnosis and analysis. However, the resulting multivolume datasets are often complex and redundant, making visualization and interpretation challenging. To address these challenges, we propose a method for fusing spectral PCCT data into a single representative volume that enables direct volume rendering and segmentation by leveraging both shared and complementary information across different channels. Our approach starts by computing 2D histograms between pairs of volumes to identify those that exhibit prominent structural features. These histograms reveal relationships and variations that may be difficult to discern from individual volumes alone. Next, we construct an extremum graph from the 2D histogram of two minimally correlated yet complementary volumes-selected to capture both shared and distinct features-thereby maximizing the information content. The graph captures the topological distribution of histogram extrema. By extracting prominent structure within this graph and projecting each grid point in histogram space onto it, we reduce the dimensionality to one, producing a unified volume. This representative volume retains key structural and material characteristics from the original spectral data while significantly reducing the analysis scope from multiple volumes to one. The result is a topology-aware, information-rich fusion of multi-energy CT datasets that facilitates more effective visualization and segmentation.

cs.HC↗

arXiv2024

Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI

Cloud formations often obscure optical satellite-based monitoring of the Earth's surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance on a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which is often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is commonly done in practice. To alleviate the COT data scarcity problem, in this work we propose a novel synthetic dataset for COT estimation, that we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the collected real dataset, code and models have been made publicly available at https://github.com/aleksispi/ml-cloud-opt-thick.

cs.CV↗

arXiv2024

Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation

Accurate segmentation of the heart is essential for personalized blood flow simulations and surgical intervention planning. Segmentations need to be accurate in every spatial dimension, which is not ensured by segmenting data slice by slice. Two cardiac computed tomography (CT) datasets consisting of 760 volumes across the whole cardiac cycle from 39 patients, and of 60 volumes from 60 patients respectively were used to train networks to simultaneously segment multiple regions representing the whole heart in 3D. The segmented regions included the left and right atrium and ventricle, left ventricular myocardium, ascending aorta, pulmonary arteries, pulmonary veins, and left atrial appendage. The widely used 3D U-Net and the UNETR architecture were compared to our proposed method optimized for large volumetric inputs. The proposed network architecture, termed Transformer Residual U-Net (TRUNet), maintains the cascade downsampling encoder, cascade upsampling decoder and skip connections from U-Net, while incorporating a Vision Transformer (ViT) block in the encoder alongside a modified ResNet50 block. TRUNet reached higher segmentation performance for all structures within approximately half the training time needed for 3D U-Net and UNETR. The proposed method achieved more precise vessel boundary segmentations and better captured the heart's overall anatomical structure compared to the other methods. The fast training time and accurate delineation of adjacent structures makes TRUNet a promising candidate for medical image segmentation tasks. The code for TRUNet is available at github.com/ljollans/TRUNet.

eess.IV↗

arXiv2019

Simultaneous laser-driven X-ray and two-photon fluorescence imaging of atomizing sprays

In this letter we report for the first time the possibility of visualizing an atomizing spray by simultaneously recording X-ray absorption and 2- photon laser-induced fluorescence imaging. This unique illumination/detection scheme was made possible thanks to the use of soft X-rays emitted from a laser-driven X-ray source. An 800 mJ laser pulse of 38 fs duration is used to generate an X-ray beam with up to 4.10^8 photons ranging from 1 to 10 keV; allowing projection radiography of water jets generated by an automotive port fuel injector. In addition, a fraction of the laser pulse (approximately 10 mJ)is employed to form a light sheet and to induce 2-photon fluorescence from a dye added to the water. The resulting high-contrast fluorescence images provide fine details of the spray structure, with reduced blur from multiple light scattering, while the integrated liquid mass is extracted from the X-ray radiography. The combination of these two highly complementary techniques, both in the visible and in the soft X-ray regime, is very promising for the future characterization of challenging spray systems as well as for further understanding the physics of liquid atomization.

physics.optics↗

arXiv2018

Proton acceleration by a pair of successive ultraintense femtosecond laser pulses

We investigate the target normal sheath acceleration of protons in thin aluminum targets irradiated at relativistic intensity by two time-separated ultrashort (35 fs) laser pulses. For identical laser pulses and target thicknesses of 3 and 6 $μ$m, we observe experimentally that the second pulse boosts the maximum energy and charge of the proton beam produced by the first pulse for time delays below $\sim0.6-1$ ps. By using two-dimensional particle-in-cell simulations we examine the variation of the proton energy spectra with respect to the time-delay between the two pulses. We demonstrate that the expansion of the target front surface caused by the first pulse significantly enhances the hot-electron generation by the second pulse arriving after a few hundreds of fs time delay. This enhancement, however, does not suffice to further accelerate the fastest protons driven by the first pulse once three-dimensional quenching effects have set in. This implies a limit to the maximum time delay that leads to proton energy enhancement, which we theoretically determine.

physics.plasm-ph↗

arXiv2017

Highly efficient angularly resolving x-ray spectrometer optimized for absorption measurements with collimated sources

Highly collimated betatron radiation from a laser wakefield accelerator is a promising tool for spectroscopic measurements. Therefore there is a requirement to create spectrometers suited to the unique properties of such a source. We demonstrate a spectrometer which achieves an energy resolution of < 5 eV at 9 keV and is angularly resolving the x-ray emission allowing the reference and spectrum to be recorded at the same time. The single photon analysis is used to significantly reduce the background noise. Theoretical performance of various configurations of the spectrometer is calculated by a ray-tracing algorithm. The properties and performance of the spectrometer including the angular and spectral resolution are demonstrated experimentally on absorption above the K-edge of a Cu foil backlit by laser-produced betatron radiation x-ray beam.

physics.ins-det↗

arXiv2012

The self-injection threshold in self-guided laser wakefield accelerators

A laser pulse traveling through a plasma can excite large amplitude plasma waves that can be used to accelerate relativistic electron beams in a very short distance---a technique called laser wakefield acceleration. Many wakefield acceleration experiments rely on the process of wavebreaking, or self-injection, to inject electrons into the wave, while other injection techniques rely on operation without self-injection. We present an experimental study into the parameters, including the pulse energy, focal spot quality and pulse power, that determine whether or not a wakefield accelerator will self-inject. By taking into account the processes of self-focusing and pulse compression we are able to extend a previously described theoretical model, where the minimum bubble size required for trapping is not constant but varies slowly with density and find excellent agreement with this model.

physics.plasm-ph↗