Numerical properties of staggered quarks with a taste-dependent mass term

The numerical properties of staggered Dirac operators with a taste-dependent mass term proposed by Adams [1,2] and by Hoelbling [3] are compared with those of ordinary staggered and Wilson Dirac operators. In the free limit and on (quenched) interacting configurations, we consider their topological properties, their spectrum, and the resulting pion mass. Although we also consider the spectral structure, topological properties, locality, and computational cost of an overlap operator with a staggered kernel, we call attention to the possibility of using the Adams and Hoelbling operators without the overlap construction. In particular, the Hoelbling operator could be used to simulate two degenerate flavors without additive mass renormalization, and thus without fine-tuning in the chiral limit.Comment: 14 pages, 9 figures. V2: published version; important note added regarding Hoelbling fermions, otherwise minor change

Limits on a Composite Higgs Boson

Precision electroweak data are generally believed to constrain the Higgs boson mass to lie below approximately 190 GeV at 95% confidence level. The standard Higgs model is, however, trivial and can only be an effective field theory valid below some high energy scale characteristic of the underlying non-trivial physics. Corrections to the custodial isospin violating parameter T arising from interactions at this higher energy scale dramatically enlarge the allowed range of Higgs mass. We perform a fit to precision electroweak data and determine the region in the (m_H, Delta T) plane that is consistent with experimental results. Overlaying the estimated size of corrections to T arising from the underlying dynamics, we find that a Higgs mass up to 500 GeV is allowed. We review two composite Higgs models which can realize the possibility of a phenomenologically acceptable heavy Higgs boson. We comment on the potential of improvements in the measurements of m_t and M_W to improve constraints on composite Higgs models.Comment: 9 pages, 2 eps figures. Shortened for PRL; some references elminate

Mathematically provable correct implementation of integrated 2D and 3D representations

representation

SDG Final Decade of Action: Resilient Pathways to Build Back Better from High-Impact Low-Probability (HILP) Events

Data Availability Statement: Not applicable.Copyright © 2022 by the authors. The 2030 Sustainable Development Goals (SDGs) offer a blueprint for global peace and prosperity, while conserving natural ecosystems and resources for the planet. However, factors such as climate-induced weather extremes and other High-Impact Low-Probability (HILP) events on their own can devastate lives and livelihoods. When a pandemic affects us, as COVID-19 has, any concurrent hazards interacting with it highlight additional challenges to disaster and emergency management worldwide. Such amplified effects contribute to greater societal and environmental risks, with cross-cutting impacts and exposing inequities. Hence, understanding how a pandemic affects the management of concurrent hazards and HILP is vital in disaster risk reduction practice. This study reviews the contemporary literature and utilizes data from the Emergency Events Database (EM-DAT) to unpack how multiple extreme events have interacted with the coronavirus pandemic and affected the progress in achieving the SDGs. This study is especially urgent, given the multidimensional societal impacts of the COVID-19 pandemic amidst climate change. Results indicate that mainstreaming risk management into development planning can mitigate the adverse effects of disasters. Successes in addressing compound risks have helped us understand the value of new technologies, such as the use of drones and robots to limit human exposure. Enhancing data collection efforts to enable inclusive sentinel systems can improve surveillance and effective response to future risk challenges. Stay-at-home policies put in place during the pandemic for virus containment have highlighted the need to holistically consider the built environment and socio-economic exigencies when addressing the pandemic’s physical and mental health impacts, and could also aid in the context of increasing climate-induced extreme events. As we have seen, such policies, services, and technologies, along with good nutrition, can significantly help safeguard health and well-being in pandemic times, especially when simultaneously faced with ubiquitous climate-induced extreme events. In the final decade of SDG actions, these measures may help in efforts to “Leave No One Behind”, enhance human–environment relations, and propel society to embrace sustainable policies and lifestyles that facilitate building back better in a post-pandemic world. Concerted actions that directly target the compounding effects of different interacting hazards should be a critical priority of the Sendai Framework by 2030.This research received no external funding

Central Collisions of Au on Au at 150, 250 and 400 A MeV

Collisions of Au on Au at incident energies of 150, 250 and 400 A MeV were studied with the FOPI-facility at GSI Darmstadt. Nuclear charge (Z < 16) and velocity of the products were detected with full azimuthal acceptance at laboratory angles of 1-30 degrees. Isotope separated light charged particles were measured with movable multiple telescopes in an angular range of 6-90 degrees. Central collisions representing about 1 % of the reaction cross section were selected by requiring high total transverse energy, but vanishing sideflow. The velocity space distributions and yields of the emitted fragments are reported. The data are analysed in terms of a thermal model including radial flow. A comparison with predictions of the Quantum Molecular Model is presented.Comment: LateX text 62 pages, plus six Postscript files with a total of 34 figures, accepted by Nucl.Phys.

Assessing the Agreement Between Eo-Based Semi-Automated Landslide Maps with Fuzzy Manual Landslide Delineation

Landslide mapping benefits from the ever increasing availability of Earth Observation (EO) data resulting from programmes like the Copernicus Sentinel missions and improved infrastructure for data access. However, there arises the need for improved automated landslide information extraction processes from EO data while the dominant method is still manual delineation. Object-based image analysis (OBIA) provides the means for the fast and efficient extraction of landslide information. To prove its quality, automated results are often compared to manually delineated landslide maps. Although there is awareness of the uncertainties inherent in manual delineations, there is a lack of understanding how they affect the levels of agreement in a direct comparison of OBIA-derived landslide maps and manually derived landslide maps. In order to provide an improved reference, we present a fuzzy approach for the manual delineation of landslides on optical satellite images, thereby making the inherent uncertainties of the delineation explicit. The fuzzy manual delineation and the OBIA classification are compared by accuracy metrics accepted in the remote sensing community. We have tested this approach for high resolution (HR) satellite images of three large landslides in Austria and Italy. We were able to show that the deviation of the OBIA result from the manual delineation can mainly be attributed to the uncertainty inherent in the manual delineation process, a relevant issue for the design of validation processes for OBIA-derived landslide maps.(VLID)458804

Automated detection of rock glaciers using deep learning and object-based image analysis

Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories

The Mass Difference Between Protons and Neutrons and the Fine Tuning of Physical Constants

We report on a large scale calculation of the origin of the mass difference between the proton and the neutron that was carried out to a large extent on the JUQUEEN computer