U(2)^5 flavor symmetry and lepton universality violation in W -> tau nu_tau

The seeming violation of universality in the tau lepton coupling to the W boson suggested by LEP II data is studied using an Effective Field Theory (EFT) approach. Within this framework we explore how this feature fits into the current constraints from electroweak precision observables using different assumptions about the flavor structure of New Physics, namely [U(2) x U(1)]^5 and U(2)^5. We show the importance of leptonic and semileptonic tau decay measurements, giving 3-4 TeV bounds on the New Physics effective scale at 90% C.L. We conclude under very general assumptions that it is not possible to accommodate this deviation from universality in the EFT framework, and thus such a signal could only be explained by the introduction of light degrees of freedom or New Physics strongly coupled at the electroweak scale.Comment: 9 pages, 2 figure

Assessing Polarimetric SAR Interferometry coherence region parameters over a permafrost landscape

Rising temperatures in the Arctic are leading to recent changes in permafrost regions. The monitoring of permafrost state and dynamics benefits from the large-scale capabilities of airborne and spaceborne remote sensing as well as the opportunity to image regions that are difficult to access. In this regard, the German Aerospace Center (DLR) conducted an experimental airborne SAR campaign aiming at characterizing SAR responses over permafrost landscapes and assessing to which extent parameters of interest, such as soil moisture and soil layer properties, can be estimated in such regions [1]. The campaign comprises two data sets acquired in the Canadian Arctic in August 2018, when the upper layer of the soil is thawed, and in March 2019, when the ground is entirely frozen. Smaller (X- and C-band) and larger (L-band) wavelengths were used. This study focuses on a particular test site encompassing the Trail Valley Creek catchment (Northwest Territories), which is a well-studied area of continuous permafrost covered by tundra vegetation [2]. Previous polarimetric analysis shows an overall decrease in backscattering power of several dB from summer to winter and a relative increase of the surface contribution (entropy/alpha analysis) [3]. Simultaneously, landscape features remain clearly recognizable at all wavelengths in winter. As permafrost parameters (vegetation, soil moisture, soil type, winter snow depth) are suspected to be related with another [2], unambiguous interpretation of the signal is challenging. Adding another dimension (interferometry) related to phase center height and therefore signal penetration depth is a step towards better interpretation of the signal. As several baselines were flown over Trail Valley Creek test site and fully polarimetric data was acquired, polarimetric SAR interferometry observables can be retrieved. A particular parameter of interest is the coherence region, namely the set of coherences that can be obtained from all possible pairs of polarimetric acquisitions at a given baseline. Pol-InSAR coherence regions are characterized by their shape and extent in the complex plane. In particular, their extent in amplitude and phase in the complex plane are of interest, as they can be related to the vertical distribution of scatterers within a given resolution cell. In summer, overall small vegetation (below 1m) covers the soil, and little penetration into the ground is expected as the upper layer of the soil is thawed. In winter, the soil is entirely frozen, covered by snow, and the vegetation is partially frozen. Larger penetration into the frozen soil is expected at larger wavelengths. These effects could influence the coherence region features. An analysis and a comparison of the Pol-InSAR coherence region parameters for the winter and summer datasets over the Trail Valley Creek catchment will be presented. Several frequencies will be considered and the influence of landcover will be assessed by discriminating between different vegetation types. [1] I. Hajnsek, H. Joerg, R. Horn, M. Keller, D. Gesswein, M. Jaeger, R. Scheiber, P. Bernhard, S. Zwieback, “DLR Airborne SAR Campaign on Permafrost Soils and Boreal Forests in the Canadian Northwest Territories, Yukon and Saskatchewan: PermASAR”, POLINSAR 2019; 9th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, 2019 [2] I. Grünberg, E.J. Wilcox, S. Zwieback, P. Marsh, and J. Boike, “Linking tundra vegetation, snow, soil temperature, and permafrost”, Biogeosciences, 17(16), 4261-4279, 2020 [3] P. Saporta, A. Alonso González, I. Hajnsek, “A temporal assessment of fully polarimetric multifrequency SAR observations over the Canadian permafrost”, EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, VDE, 202

Constrained Tensor Decompositions for Polarimetric Time Series Change Analysis

Synthetic Aperture Radar (SAR) sensors provide data in polarimetric, interferometric, temporal, and spatial dimensions depending on the acquisition setup. With the increasing availability of multi-dimensional SAR data the joint processing and information extraction from several data dimensions grows in relevance. Some existing works already combine two data dimensions for different tasks. For example, forest height inversion methods [2] jointly use polarimetry and interferometry, the Sum of Kronecker Products (SKP) decomposition [3] exploits polarimetry and tomography, polarimetric change detection [1] combines the polarimetric and temporal dimensions, and convolutional neural networks [4] for land cover classification can use spatial and polarimetric features. We propose a decomposition framework for multi-dimensional SAR data that allows an arbitrary number of data dimensions and is based on the Canonical Polyadic (CP) tensor decomposition. The decomposition is formulated as an optimization problem allowing precise control over the shape and properties of the output factors. We take into account the specifics of SAR data by adding constraints for physical validity and interpretability. In order to demonstrate our approach, we formulate a decomposition for polarimetric time series using the proposed framework. The algorithm decomposes a stack of polarimetric coherency matrices into R components, each defined by a polarimetric and a temporal factor. We then use the factors to evaluate F-SAR data in X, C, and L bands obtained over agricultural areas during the CROPEX 2014 campaign. We analyze the evolution of four different crop types and the changes in the signal related to the growth, drying, fruit maturation, and harvest. The obtained factors describe the changes in the crops in a compact way and show correlations to certain crop parameters. The results are visualized using the polarimetric change matrices proposed in [1] and show additional fine-grained changes in comparison to the original method. The decomposition framework is an extensible and promising tool for joint information extraction from multi-dimensional SAR data. It can be used to improve existing methods or extend them with new data dimensions. For example, implementing the SKP decomposition using the framework allows to obtain more than two components, or enables to integrate an additional data dimension such as time. Furthermore, it is possible to integrate physical models into the data-driven tensor decomposition approach. The framework implementation builds on top of PyTorch and supports automatic differentiation and optimization in the complex domain. This simplifies the decomposition design, facilitates experiments with different data dimensions, and allows to concentrate on the choice of the constraints or interpretation of the factors. [1] Alberto Alonso-González et al. "Polarimetric SAR Time Series Change Analysis over Agricultural Areas". In: IEEE Transactions on Geoscience and Remote Sensing 58.10 (2020), pp. 7317-7330. [2] SR Cloude and KP Papathanassiou. "Three-stage inversion process for polarimetric SAR interferometry". In: IEE Proceedings-Radar, Sonar and Navigation 150.3 (2003), pp. 125-134. [3] Stefano Tebaldini. "Algebraic Synthesis of Forest Scenarios from Multibaseline PolInSAR Data". In: IEEE Transactions on Geoscience and Remote Sensing 47.12 (2009), pp. 4132-4142. [4] Xiao Xiang Zhu et al. "Deep learning meets SAR: Concepts, models, pitfalls, and perspectives". In: IEEE Geoscience and Remote Sensing Magazine 9.4 (2021), pp. 143-172

Constrained Tensor Decompositions for SAR Data: Agricultural Polarimetric Time Series Analysis

Tensor decompositions are a powerful tool for multidimensional data analysis, interpretation, and signal processing. This work develops a constrained tensor decomposition framework for complex multidimensional Synthetic Aperture Radar (SAR) data. The framework generalizes the Canonical Polyadic (CP) decomposition by formulating it as an optimization problem and allows precise control over the shape and properties of the output factors. The implementation supports complex tensors, automatic differentiation, different loss functions, and optimizers. We discuss the importance of constraints for physical validity, interpretability, and uniqueness of the decomposition results. To illustrate the framework, we formulate a polarimetric time series decomposition and apply it to data acquired over agricultural areas to analyze the development of four crop types at X, C, and L bands over the period of twelve weeks. The obtained temporal factors describe the changes in the crops in a compact way and show a correlation to certain crop parameters. We extend the existing polarimetric time series change analysis with the decomposition to show the changes in more detail and provide an interpretation through the polarimetric factors. The decomposition framework is extensible and promising for joint information extraction from multidimensional SAR data

Multi-Baseline Pol-InSAR Inversion of the Subsurface Scattering Structure of Ice Sheets

The influence of the subsurface properties of ice sheets on polarimetric synthetic aperture radar interferometry (Pol-InSAR) measurements is well known. In order to invert this relationship for the extraction of geophysical parameters from Pol-InSAR data, models of the subsurface scattering structure are required. One potential application is the estimation of the penetration bias in interferometric surface elevation measurements of ice sheets, which was demonstrated based on single-baseline data. However, the model complexity and performance are constrained by the limited observation space. This study, therefore, investigates the inversion of subsurface scattering structures with multi-baseline fully polarimetric Pol-InSAR data, which allows accounting for more realistic scattering scenarios. Preliminary results indicate a more robust inversion of the penetration bias compared to the single-baseline case

Parallel signal detection for generalized spatial modulation MIMO systems

[EN] Generalized Spatial Modulation is a recently developed technique that is designed to enhance the efficiency of transmissions in MIMO Systems. However, the procedure for correctly retrieving the sent signal at the receiving end is quite demanding. Specifically, the computation of the maximum likelihood solution is computationally very expensive. In this paper, we propose a parallel method for the computation of the maximum likelihood solution using the parallel computing library OpenMP. The proposed parallel algorithm computes the maximum likelihood solution faster than the sequential version, and substantially reduces the worst-case computing times.This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities and by the European Union through grant RTI2018- 098085-BC41 (MCUI/AEI/FEDER), by GVA through PROMETEO/2019/109, and by RED 2018-102668-T. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.García Mollá, VM.; Simarro, MA.; Martínez Zaldívar, FJ.; Boratto, M.; Alonso-Jordá, P.; Gonzalez, A. (2022). Parallel signal detection for generalized spatial modulation MIMO systems. The Journal of Supercomputing. 78(5):7059-7077. https://doi.org/10.1007/s11227-021-04163-y7059707778