arXiv subjects

Michael Schmitt

Publications and source records attributed to Michael Schmitt.

At least 55 records · Page 3Linked to original sources

arXiv2019

Bayesian Block Histogramming for High Energy Physics

The Bayesian Block algorithm, originally developed for applications in astronomy, can be used to improve the binning of histograms in high energy physics. The visual improvement can be dramatic, as shown here with two simple examples. More importantly, this algorithm and the histogram is produces is a non-parametric density estimate, providing a description of background distributions that does not suffer from the arbitrariness of ad hoc analytical functions. The statistical power of an hypothesis test based on Bayesian Blocks is nearly as good as that obtained by fitting analytical functions. Two examples are provided: a narrow peak on a smoothly-falling background, and an excess in the tail of a background that falls rapidly over several orders of magnitude. These examples show the usefulness of the binning provided by the Bayesian Blocks algorithm both for presentation of data and when searching for new physics.

physics.data-an↗

arXiv2019

Branching fraction for $Z$ decays to four leptons and constraints on new physics

The LHC experiments have measured the branching fraction for $Z$ decays to four leptons (electrons or muons). We have combined these measurements with the result $\mathcal{B}(Z \to 4\ell) = (4.58 \pm 0.26) \times 10^{-6}$, allowing a precise comparison to the standard model prediction of $(4.70 \pm 0.03) \times 10^{-6}$. We use a minimal extension of the standard model to demonstrate that this combined value may be used to set stringent limits on new physics.

hep-ex↗

arXiv2019

Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of "dark" and "bright" appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection.

eess.IV↗

arXiv2018

Fusion of Urban TanDEM-X raw DEMs using variational models

Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes < 20%) and better than 4m for rugged terrain (slopes > 20%) was created through the TanDEM-X mission. One important step of the chain of global DEM generation is to mosaic and fuse multiple raw DEM tiles to reach the target height accuracy. Currently, Weighted Averaging (WA) is applied as a fast and simple method for TanDEM-X raw DEM fusion in which the weights are computed from height error maps delivered from the Interferometric TanDEM-X Processor (ITP). However, evaluations show that WA is not the perfect DEM fusion method for urban areas especially in confrontation with edges such as building outlines. The main focus of this paper is to investigate more advanced variational approaches such as TV-L1 and Huber models. Furthermore, we also assess the performance of variational models for fusing raw DEMs produced from data takes with different baseline configurations and height of ambiguities. The results illustrate the high efficiency of variational models for TanDEM-X raw DEM fusion in comparison to WA. Using variational models could improve the DEM quality by up to 2m particularly in inner-city subsets.

eess.IV↗

arXiv2018

A Framework for SAR-Optical Stereogrammetry over Urban Areas

Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverage, and spectral imaging ability, fusion techniques are required to integrate the different properties of each sensor and produce useful information. For example, synthetic aperture radar (SAR) data can be fused with optical imagery to produce 3D information using stereogrammetric methods. The main focus of this study is to investigate the possibility of applying a stereogrammetry pipeline to very-high-resolution (VHR) SAR-optical image pairs. For this purpose, the applicability of semi-global matching is investigated in this unconventional multi-sensor setting. To support the image matching by reducing the search space and accelerating the identification of correct, reliable matches, the possibility of establishing an epipolarity constraint for VHR SAR-optical image pairs is investigated as well. In addition, it is shown that the absolute geolocation accuracy of VHR optical imagery with respect to VHR SAR imagery such as provided by TerraSAR-X can be improved by a multi-sensor block adjustment formulation based on rational polynomial coefficients. Finally, the feasibility of generating point clouds with a median accuracy of about 2m is demonstrated and confirms the potential of 3D reconstruction from SAR-optical image pairs over urban areas.

eess.IV↗

arXiv2018

Fusion of TanDEM-X and Cartosat-1 Elevation Data Supported by NeuralNetwork-Predicted Weight Maps

Recently, the bistatic SAR interferometry mission TanDEM-X provided a global terrain map with unprecedented accuracy. However, visual inspection and empirical assessment of TanDEM-X elevation data against high-resolution ground truth illustrates that the quality of the DEM decreases in urban areas because of SAR-inherent imaging properties. One possible solution for an enhancement of the TanDEM-X DEM quality is to fuse it with other elevation data derived from high-resolution optical stereoscopic imagery, such as that provided by the Cartosat-1 mission. This is usually done by Weighted Averaging (WA) of previously aligned DEM cells. The main contribution of this paper is to develop a method to efficiently predict weight maps in order to achieve optimized fusion results. The prediction is modeled using a fully connected Artificial Neural Network (ANN). The idea of this ANN is to extract suitable features from DEMs that relate to height residuals in training areas and then to automatically learn the pattern of the relationship between height errors and features. The results show the DEM fusion based on the ANN-predicted weights improves the qualities of the study DEMs. Apart from increasing the absolute accuracy of Cartosat-1 DEM by DEM fusion, the relative accuracy (respective to reference LiDAR data) ofDEMs is improved by up to 50% in urban areas and 22% in non-urban areas while the improvement by them-based method does not exceed 20% and 10% in urban and non-urban areas respectively.

eess.IV↗

arXiv2018

The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion

While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing. In particular, this holds for applications involving data from multiple sensors with heterogeneous characteristics. One example for that is the fusion of synthetic aperture radar (SAR) data and optical imagery. With this paper, we publish the SEN1-2 dataset to foster deep learning research in SAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding image patches, collected from across the globe and throughout all meteorological seasons. Besides a detailed description of the dataset, we show exemplary results for several possible applications, such as SAR image colorization, SAR-optical image matching, and creation of artificial optical images from SAR input data. Since SEN1-2 is the first large open dataset of this kind, we believe it will support further developments in the field of deep learning for remote sensing as well as multi-sensor data fusion.

cs.CV↗

arXiv2018

Shining Light on the Mass Scale and Nature of Neutrinos with $eγ\to eν\overlineν$

The discovery of neutrino oscillations invites many fundamental physics questions that have yet to be answered. Two of these questions are simple, easy to state, and essential: What are the values of the neutrino masses? Are neutrinos Majorana fermions? The reason we don't know the answer to those questions is that it is difficult to measure neutrino properties outside of the ultrarelativistic regime. We discuss the physics of $eγ\to eν\barν$ near threshold, where one has access to nonrelativistic neutrinos and only nonrelativistic neutrinos. Near threshold, $eγ\to eν\barν$ is a rich phenomenon and its cross section is sensitive to the individual values of the neutrino masses and the nature of the neutrinos. We show that if one could scan the threshold region, it would be simple to identify the mass of the lightest neutrino, the neutrino mass ordering, and whether the neutrinos are Majorana fermions. In practice, however, event rates are tiny and backgrounds are huge; the observation of $eγ\to eν\barν$ in the sub-eV regime appears to be utterly inaccessible in the laboratory. Our results, nonetheless, effectively illustrate the discriminatory power of nonrelativistic neutrino observables.

hep-ph↗

arXiv2018

Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established handcrafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar (SAR), optical images, remote sensing, data fusion, stereogrammetry

eess.IV↗

arXiv2018

Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN

In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure. Index Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matching

eess.IV↗

arXiv2018

Object-based Multipass InSAR via Robust Low Rank Tensor Decomposition

The most unique advantage of multipass SAR interferometry (InSAR) is the retrieval of long term geophysical parameters, e.g. linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed in [1], as an alternative to the typical single-pixel methods, e.g. Persistent Scatterer Interferometry (PSI), or pixel-cluster-based methods, e.g. SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a followon, this paper investigates the inherent low rank property of such phase tensors, and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition (RoMIO). We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g. PSI, by a factor of ten to thirty in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits in turn can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.

eess.IV↗

arXiv2017

The Use of Minimal Spanning Trees in Particle Physics

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

stat.AP↗

arXiv2014

Using Random Forests to Classify W+W- and ttbar Events

We have carried out an exercise in the classification of W+W- and ttbar events as produced in a high-energy proton-proton collider, motivated in part by the current tension between the measured and predicted values of the WW cross section. The performance of the random forest classifier surpasses that of a standard cut-based analysis. Furthermore, the distortion of the distributions of key kinematic event features is relatively slight, suggesting that systematic uncertainties due to modeling might be reduced. Finally, our random forest can tolerate missing features such as missing transverse energy without a severe degradation of its performance.

hep-ex↗

arXiv2012

Enhanced Rare Pion Decays from a Model of MeV Dark Matter

A model has been proposed in which neutral scalar particles chi, of mass 1-10 MeV, annihilate through the exchange of a light vector boson U, of mass 10-100 MeV, to produce the 511 keV line observed emanating from the center of the galaxy. The chi interacts weakly with normal matter and is a viable dark matter candidate. If the U-boson couples to quarks as well as to electrons, it could enhance the branching ratio for the rare decay pi0 -> e+e-. A recent measurement by the KTeV Collaboration lies three standard deviations above a prediction by Dorokhov and Ivanov, and we relate this excess to the couplings of the U-boson. The values are consistent with other constraints and considerations. We make some comments on possible improvements in the data.

hep-ph↗

arXiv2008

Electron - Dark Matter Scattering in an Evacuated Tube

The light dark matter model can explain both the primordial abundance of dark matter and the anomalous 511 keV gamma-ray signal from the galactic center. This model posits a light neutral scalar, χ, with a mass in the range 1 MeV < Mchi < 10 MeV, as well as a light neutral spin-1 boson, U, which mediates the annihilation channel χχ-> e+e-. Since the dark matter particle is light, its number density is relatively large if it accounts for a local dark matter density of 0.3 GeV/cm^3. We consider an experiment in which a low-energy, high-current electron beam is passed through a long evacuated tube, and elastic scattering of electrons off dark matter particles is observed. The kinematics of this process allow a clean separation of the signal process from scattering off residual gas in the tube, and also a direct measurement of Mchi.

hep-ex↗

arXiv2008

Spectroscopy of formaldehyde in the 30140-30790cm^-1 range

Room-temperature absorption spectroscopy of formaldehyde has been performed in the 30140-30790cm^-1 range. Using tunable ultraviolet continuous-wave laser light, individual rotational lines are well resolved in the Doppler-broadened spectrum. Making use of genetic algorithms, the main features of the spectrum are reproduced. Spectral data is made available as Supporting Information.

physics.chem-ph↗

arXiv2007

A Method for the Precision Mass Measurement of the Stop Quark at the International Linear Collider

Many supersymmetric models predict new particles within the reach of the next generation of colliders. For an understanding of the model structure and the mechanism(s) of symmetry breaking, it is important to know the masses of the new particles precisely. In this article the measurement of the mass of the scalar partner of the top quark (stop) at an e+e- collider is studied. A relatively light stop is motivated by attempts to explain electroweak baryogenesis and can play an important role in dark matter relic density. A method is presented which makes use of cross-section measurements near the pair-production threshold as well as at higher center-of-mass energies. It is shown that this method not only increases the statistical precision, but also greatly reduces the systematic uncertainties, which can be important. Numerical results are presented, based on a realistic event simulation, for two signal selection strategies: using conventional selection cuts, and using an Iterative Discriminant Analysis (IDA). Our studies indicate that a precision of $Δ\mstop = 0.42$ GeV can be achieved, representing a major improvement over previous studies. While the analysis of stops is particularly challenging due to the possibility of stop hadronization, the general procedure could be applied to the mass measurement of other particles as well. We also comment on the potential of the IDA to discover a stop quark in this scenario, and we revisit the accuracy of the theoretical predictions for the neutralino relic density

hep-ph↗

arXiv2007

An Experiment to Search for Light Dark Matter in Low-Energy ep Scattering

Anomalous production of low-energy photons from the galactic center have fueled speculations on the nature and properties of dark matter particles. In particular, it has been proposed that light scalars may be responsible for the bulk of the matter density of the universe, and that they couple to ordinary matter through a light spin-1 boson. If this is the case, then such particles may be produced in the quasi-elastic low-energy scattering of electrons off protons. We present a proposal for an experiment to search for this process and assess its viability.

hep-ex↗