スパースアレイ・レーダにおける信号処理技術

Tohoku University佐藤源之課

Unfolding the spectrum of the blazar Markarian 421 using data from the first Large-Sized Telescope of the Cherenkov Telescope Array

The nature of acceleration processes of very-high-energy radiation in the universe is largely unsolved. To study these, Imaging Atmospheric Cherenkov Telescopes measure Cherenkov radiation produced by secondary particles in Extensive Air Showers. The Cherenkov Telescope Array (CTA) is the next generation of ground-based Cherenkov astronomy to be built in the coming years parts on La Palma, Spain and parts at Paranal Observatory, Chile. The first prototype telescope of CTA, the LST-1, was inaugurated in 2018 on La Palma, and has been in the commissioning phase since. In this thesis, I develop a new automated analysis pipeline for analyses of point-like gamma-ray sources for LST-1 observations, using the workflow manager snakemake. I implement regularized unfolding in the open-source high-level gamma-ray analysis software Gammapy. I use the analysis pipeline and the unfolding implementation to analyze data of the blazar Markarian 421, creating an unfolded energy spectrum. The unfolded energy spectrum calculated in this thesis is compared to earlier analyses. It extends the measured range of the multi-wavelength energy spectrum of Markarian 421 to the highest energies.Die Natur der Beschleunigungsmechanismen für sehr hochenergetische Strahlung im Universum ist weitestgehend ungeklärt. Um diese zu untersuchen, werden bildgebende, atmosphärische Tscherenkow Teleskope eingesetzt, die Tscherenkowstrahlung messen, die von Sekundärteilchen in ausgedehnten Luftschauern erzeugt wird. Das Cherenkov Telescope Array (CTA) ist die nächste Generation der bodengebundenen Tscherenkow-Astronomie und wird in den kommenden Jahren teils auf La Palma, Spanien, teils im Paranal Observatory, Chile, gebaut. Das erste Prototyp-Teleskop des CTA, das LST-1, wurde 2018 auf La Palma eingeweiht und befindet sich seitdem in der Phase der Inbetriebnahme. In dieser Arbeit entwickle ich eine neue automatisierte Analysepipeline für Analysen von punktartigen kosmischen Gammastrahlungsquellen für LST-1-Beobachtungen unter Verwendung des Workflow-Managers snakemake. Ich implementiere regularisiertes Entfalten in der quelloffenen Gammastrahlenanalysesoftware Gammapy. Ich verwende die Analysepipeline und die Entfalungsimplementierung, um Daten des Blazars Markarian 421 zu analysieren und ein entfaltetes Energiespektrum zu erstellen. Das in dieser Arbeit entfaltete Energiespektrum wird mit früheren Analysen verglichen und erweitert den gemessenen Bereich des multiwellenlängen Energiespektrums von Markarian 421 bis zu den höchsten Energien

Investigation of Sea Ice Using Multiple Synthetic Aperture Radar Acquisitions

The papers of this thesis are not available in Munin. Paper I: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Tomographic imaging of fjord ice using a very high resolution ground-based SAR system. Available in IEEE Transactions on Geoscience and Remote Sensing, 55 (2):698-714. Paper II: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Lake and fjord ice imaging using a multifrequency ground-based tomographic SAR system. Available in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10):4457-4468. Paper III: Yitayew, T. G., Divine, D. V., Dierking, W., Eltoft, T., Ferro-Famil, L., Rosel, A. & Negrel, J. Validation of Sea ice Topographic Heights Derived from TanDEMX Interferometric SAR Data with Results from Laser Profiler and Photogrammetry. (Manuscript).The thesis investigates imaging in the vertical direction of different types of ice in the arctic using synthetic aperture radar (SAR) tomography and SAR interferometry. In the first part, the magnitude and the positions of the dominant scattering contributions within snow covered fjord and lake ice layers are effectively identified by using a very high resolution ground-based tomographic SAR system. Datasets collected at multiple frequencies and polarizations over two test sites in Tromsø area, northern Norway, are used for characterizing the three-dimensional response of snow and ice. The presented experimental results helped to improve our understanding of the interaction between radar waves and snow and ice layers. The reconstructed radar responses are also used for estimating the refractive indices and the vertical positions of the different sub-layers of snow and ice. The second part of the thesis deals with the retrieval of the surface topography of multi-year sea ice using SAR interferometry. Satellite acquisitions from TanDEM-X over the Svalbard area are used for analysis. The retrieved surface height is validated by using overlapping helicopter-based stereo camera and laser profiler measurements, and a very good agreement has been found. The work contributes to an improved understanding regarding the potential of SAR tomography for imaging the vertical scattering distribution of snow and ice layers, and for studying the influence of both sensor parameters such as its frequency and polarization and scene properties such as layer stratification, air bubbles and small-scale roughness of the interfaces on snow and ice backscattered signal. Moreover, the presented results reveal the potential of SAR interferometry for retrieving the surface topography of sea ice

Frequency domain high density diffuse optical tomography for functional brain imaging

Measurements of dynamic near-infrared (NIR) light attenuation across the human head together with model-based image reconstruction algorithms allow the recovery of three-dimensional spatial brain activation maps. Previous studies using high-density diffuse optical tomography (HD-DOT) systems have reported improved image quality over sparse arrays. Modulated NIR light, known as Frequency Domain (FD) NIR, enables measurements of phase shift along with amplitude attenuation. It is hypothesised that the utilization of these two sets of complementary data (phase and amplitude) for brain activity detection will result in an improvement in reconstructed image quality within HD-DOT. However, parameter recovery in DOT is a computationally expensive algorithm, especially when FD-HD measurements are required over a large and complex volume, as in the case of brain functional imaging. Therefore, computational tools for the light propagation modelling, known as the forward model, and the parameter recovery, known as the inverse problem, have been developed, in order to enable FD-HD-DOT. The forward model, within a diffusion approximation-based finite-element modelling framework, is accelerated by employing parallelization. A 10-fold speed increase when GPU architectures are available is achieved while maintaining high accuracy. For a very high-resolution finite-element model of the adult human head with ∼600,000 nodes, light propagation can be calculated at ∼0.25s per excitation source. Additionally, a framework for the sparse formulation of the inverse model, incorporating parallel computing, is proposed, achieving a 10-fold speed increase and a 100-fold memory efficiency, whilst maintaining reconstruction quality. Finally, to evaluate image reconstruction with and without the additional phase information, point spread functions have been simulated across a whole-scalp field of view in 24 subject-specific anatomical models using an experimentally derived noise model. The addition of phase information has shown to improve the image quality by reducing localization error by up to 59%, effective resolution by up to 21%, and depth penetration up to 5mm, as compared to using the intensity attenuation measurements alone. In addition, experimental data collected during a retinotopic experiment reveal that the phase data contains unique information about brain activity and enables images to be resolved for deeper brain regions

Measuring blood flow and pro-inflammatory changes in the rabbit aorta

Atherosclerosis is a chronic inflammatory disease that develops as a consequence of progressive entrapment of low density lipoprotein, fibrous proteins and inflammatory cells in the arterial intima. Once triggered, a myriad of inflammatory and atherogenic factors mediate disease progression. However, the role of pro-inflammatory activity in the initiation of atherogenesis and its relation to altered mechanical stresses acting on the arterial wall is unclear. Estimation of wall shear stress (WSS) and the inflammatory mediator NF-κB is consequently useful. In this thesis novel ultrasound tools for accurate measurement of spatiotemporally varying 2D and 3D blood flow, with and without the use of contrast agents, have been developed. This allowed for the first time accurate, broad-view quantification of WSS around branches of the rabbit abdominal aorta. A thorough review of the evidence for a relationship between flow, NF-κB and disease was performed which highlighted discrepancies in the current literature and was used to guide the study design. Subsequently, methods for the measurement and colocalization of the spatial distribution of NF-κB, arterial permeability and nuclear morphology in the aorta of New Zealand White rabbits were developed. It was demonstrated that endothelial pro-inflammatory changes are spatially correlated with patterns of WSS, nuclear morphology and arterial permeability in vivo in the rabbit descending and abdominal aorta. The data are consistent with a causal chain between WSS, macromolecule uptake, inflammation and disease, and with the hypothesis that lipids are deposited first, through flow-mediated naturally occurring transmigration that, in excessive amounts, leads to subsequent inflammation and disease.Open Acces

Discovery in Physics

Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement

Statistical methods for sparse functional object data: elastic curves, shapes and densities

Many applications naturally yield data that can be viewed as elements in non-linear spaces. Consequently, there is a need for non-standard statistical methods capable of handling such data. The work presented here deals with the analysis of data in complex spaces derived from functional L2-spaces as quotient spaces (or subsets of such spaces). These data types include elastic curves represented as d-dimensional functions modulo re-parametrization, planar shapes represented as 2-dimensional functions modulo rotation, scaling and translation, and elastic planar shapes combining all of these invariances. Moreover, also probability densities can be thought of as non-negative functions modulo scaling. Since these functional object data spaces lack a natural Hilbert space structure, this work proposes specialized methods that integrate techniques from functional data analysis with those for metric and manifold data. In particular, but not exclusively, novel regression methods for specific metric quotient spaces are discussed. Special attention is given to handling discrete observations, since in practice curves and shapes are typically observed only as a discrete (often sparse or irregular) set of points. Similarly, density functions are usually not directly observed, but a (small) sample from the corresponding probability distribution is available. Overall, this work comprises six contributions that propose new methods for sparse functional object data and apply them to relevant real-world datasets, predominantly in a biomedical context