Hasil untuk "Physics"

A. Becke

J. Kok, E. Parteli, T. Michaels et al.

The transport of sand and dust by wind is a potent erosional force, creates sand dunes and ripples, and loads the atmosphere with suspended dust aerosols. This paper presents an extensive review of the physics of wind-blown sand and dust on Earth and Mars. Specifically, we review the physics of aeolian saltation, the formation and development of sand dunes and ripples, the physics of dust aerosol emission, the weather phenomena that trigger dust storms, and the lifting of dust by dust devils and other small-scale vortices. We also discuss the physics of wind-blown sand and dune formation on Venus and Titan.

S. Hartnoll

These notes are loosely based on lectures given at the CERN Winter School on Supergravity, Strings and Gauge theories, February 2009, and at the IPM String School in Tehran, April 2009. I have focused on a few concrete topics and also on addressing questions that have arisen repeatedly. Background condensed matter physics material is included as motivation and easy reference for the high energy physics community. The discussion of holographic techniques progresses from equilibrium, to transport and to superconductivity.

J. Eggers, E. Villermaux

P. Blom, V. Mihailetchi, L. Koster et al.

T. Sakai, S. Sugimoto

We present a holographic dual of four-dimensional, large N_c QCD with massless flavors. This model is constructed by placing N_f probe D8-branes into a D4 background, where supersymmetry is completely broken. The chiral symmetry breaking in QCD is manifested as a smooth interpolation of D8 - anti-D8 pairs in the supergravity background. The meson spectrum is examined by analyzing a five-dimensional Yang-Mills theory that originates from the non-Abelian DBI action of the probe D8-brane. It is found that our model yields massless pions, which are identified with Nambu-Goldstone bosons associated with the chiral symmetry breaking. We obtain the low-energy effective action of the pion field and show that it contains the usual kinetic term of the chiral Lagrangian and the Skyrme term. A brane configuration that defines a dynamical baryon is identified with the Skyrmion. We also derive the effective action including the lightest vector meson. Our model is closely related to that in the hidden local symmetry approach, and we obtain a Kawarabayashi-Suzuki-Riazuddin-Fayyazuddin-type relation among the couplings. Furthermore, we investigate the Chern-Simons term on the probe brane and show that it leads to the Wess-Zumino-Witten term. The mass of the \eta' meson is also considered, and we formulate a simple derivation of the \eta' mass term satisfying the Witten-Veneziano formula from supergravity.

A. Gossard, D. Awschalom, Roberto C. Myers

M. Le Breton, A. Bacak, J. Muller et al.

European scale harmonized monitoring of atmospheric composition was initiated in the early 1970s, and the activity has generated a comprehensive dataset (available at http://www.emep.int) which allows the evaluation of regional and spatial trends of air pollution during a period of nearly 40 years. Results from the monitoring made within EMEP, the European Monitoring and Evaluation Programme, show large reductions in ambient concentrations and deposition of sulphur species during the last decades. Reductions are in the order of 70–90% since the year 1980, and correspond well with reported emission changes. Also reduction in emissions of nitrogen oxides (NOx) are reflected in the measurements, with an average decrease of nitrogen dioxide and nitrate in precipitation by about 23% and 25% respectively since 1990. Only minor reductions are however seen since the late 1990s. The concentrations of total nitrate in air have decreased on average only by 8% since 1990, and fewer sites show a significant trend. A majority of the EMEP sites show a decreasing trend in reduced nitrogen both in air and precipitation on the order of 25% since 1990. Deposition of base cations has decreased during the past 30 years, and the pH in precipitation has increased across Europe. Large inter annual variations in the particulate matter mass concentrations reflect meteorological variability, but still there is a relatively clear overall decrease at several sites during the last decade. With few observations going back to the 1990s, the observed chemical composition is applied to document a change in particulate matter (PM) mass even since 1980. These data indicate an overall reduction of about 5 μg/m3 from sulphate alone. Despite the significant reductions in sulphur emissions, sulphate still remains one of the single most important compounds contributing to regional scale aerosol mass concentration. Long-term ozone trends at EMEP sites show a mixed pattern. The year-to-year variability in ozone due to varying meteorological conditions is substantial, making it hard to separate the trends caused by emission change from other effects. For the Nordic countries the data indicate a reduced occurrence of very low concentrations. The most pronounced change in the frequency distribution is seen at sites in the UK and the Netherlands, showing a reduction in the higher values. Smaller changes are seen in Germany, while in Switzerland and Austria, no change is seen in the frequency distribution of ozone. The lack of long-term data series is a major obstacle for studying trends in volatile organic compounds (VOC). The scatter in the data is large, and significant changes are only found for certain components and stations. Concentrations of the heavy metals lead and cadmium have decreased in both air and precipitation during the last 20 years, with reductions in the order of 80–90% for Pb and 64–84% for Cd (precipitation and air respectively). The measurements of total gaseous mercury indicate a dramatic decrease in concentrations during 1980 to about 1993. Trends in hexachlorocyclohexanes (HCHs) show a significant decrease in annual average air concentrations. For other persistent organic pollutants (POPs) the patterns is mixed, and differs between sites and between measurements in air versus precipitation.

H. Nilles

S. Woosley, A. Heger, T. Weaver

C. Cercignani

D. Zang, S. Tarafdar, Y. Tarasevich et al.

Abstract Evaporation of a drop, though a simple everyday observation, provides a fascinating subject for study. Various issues interact here, such as dynamics of the contact line, evaporation-induced phase transitions, and formation of patterns. The explanation of the rich variety of patterns formed is not only an academic challenge, but also a problem of practical importance, as applications are growing in medical diagnosis and improvement of coating/printing technology. The multi-scale aspect of the problem is emphasized in this review. The specific fundamental problem to be solved, related to the system is the investigation of the mass transfer processes, the formation and evolution of phase fronts and the identification of mechanisms of pattern formation. To understand these problems, we introduce the important forces and interactions involved in these processes, and highlight the evaporation-driven phase transitions and flows in the drop. We focus on how the deposited patterns are related to and tuned by important factors, for instance substrate properties and contents of the drop. In addition, the formation of crust and crack patterns are discussed. The simulation and modeling methods, which are often utilized in this topic, are also reviewed. Finally, we summarize the applications of drop evaporation and suggest several potential directions for future research in this area. Exploiting the full potential of this topic in basic science research and applications needs involvement and interaction between scientists and engineers from disciplines of physics, chemistry, biology, medicine and other related fields.

Yibo Yang, P. Perdikaris

Abstract We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as surrogates of physical systems in which the cost of data acquisition is high, and training data-sets are typically small. This provides a flexible framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations that entirely bypasses the need for repeatedly sampling expensive experiments or numerical simulators. We demonstrate the effectiveness of our approach through a series of examples involving uncertainty propagation in non-linear conservation laws, and the discovery of constitutive laws for flow through porous media directly from noisy data.

J. Rumble

N. Jia, Jing Cao, X. Tan et al.

Abstract Thermoelectrics is attractive as a green and sustainable way for harnessing waste heat and cooling applications. Designing high performance thermoelectrics involves navigating the complex interplay between electronic and heat transports. This fundamentally involves understanding the scattering physics of both electrons and phonons, as well as maximizing symmetry-breaking in entropy and electronic transports. In the last two decades, thermoelectrics have progressed in leaps and bounds thanks to parallel advancements in scientific technologies and physical understandings. Figure of merit zT of 2 and above have been consistently reported in various materials, especially Chalcogenides. In this review, we provide a broad picture of physically driven optimization strategies for thermoelectric materials, with emphasis on electronic transport aspect of inorganic materials. We also discuss and analyzes various newly coined metrics such as quality factors, electronics quality factor, electronic fitness function, weighted mobility, and Fermi surface complexity factor. More importantly, we look at the non-trivial interdependencies between various physical parameters even at a very fundamental level. Moving forward, we discuss the outlook for the potential of 3D printing and device oriented research in thermoelectrics. The intuition derived from this review will be useful not only to guide materials selection, but also research directions in the coming years.

L. Anchordoqui, A. Ariga, T. Ariga et al.

The Forward Physics Facility (FPF) is a proposal to create a cavern with the space and infrastructure to support a suite of far-forward experiments at the Large Hadron Collider during the High Luminosity era. Located along the beam collision axis and shielded from the interaction point by at least 100 m of concrete and rock, the FPF will house experiments that will detect particles outside the acceptance of the existing large LHC experiments and will observe rare and exotic processes in an extremely low-background environment. In this work, we summarize the current status of plans for the FPF, including recent progress in civil engineering in identifying promising sites for the FPF and the experiments currently envisioned to realize the FPF's physics potential. We then review the many Standard Model and new physics topics that will be advanced by the FPF, including searches for long-lived particles, probes of dark matter and dark sectors, high-statistics studies of TeV neutrinos of all three flavors, aspects of perturbative and non-perturbative QCD, and high-energy astroparticle physics.

J. Aalbers, K. Abe, V. Aerne et al.

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.

D. Xiao, Fayin Wang, Z. Dai

In 2007, a very bright radio pulse was identified in the archival data of the Parkes Telescope in Australia, marking the beginning of a new research branch in astrophysics. In 2013, this kind of millisecond bursts with extremely high brightness temperature takes a unified name, fast radio burst (FRB). Over the first few years, FRBs seemed very mysterious because the sample of known events was limited. With the improvement of instruments over the last five years, hundreds of new FRBs have been discovered. The field is now undergoing a revolution and understanding of FRB has rapidly increased as new observational data increasingly accumulate. In this review, we will summarize the basic physics of FRBs and discuss the current research progress in this area. We have tried to cover a wide range of FRB topics, including the observational property, propagation effect, population study, radiation mechanism, source model, and application in cosmology. A framework based on the latest observational facts is now under construction. In the near future, this exciting field is expected to make significant breakthroughs.

G. Karagiorgi, G. Kasieczka, S. Kravitz et al.

Compelling experimental evidence suggests the existence of new physics beyond the well-established and tested standard model of particle physics. Various current and upcoming experiments are searching for signatures of new physics. Despite the variety of approaches and theoretical models tested in these experiments, what they all have in common is the very large volume of complex data that they produce. This data challenge calls for powerful statistical methods. Machine learning has been in use in high-energy particle physics for well over a decade, but the rise of deep learning in the early 2010s has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present Review, which discusses methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare event searches and neutrino experiments. Owing to the growing volumes of data from high-energy physics experiments, modern deep learning methods are playing an increasingly important role in all aspects of data taking and analysis. This Review provides an overview of key developments, with a focus on the search for physics beyond the standard model. There have been large and sustained developments of deep learning in high-energy physics over the past several years. Supervised machine learning methods are widely used to identify known particles and to design targeted searches for specific theories of new physics. Less-than-supervised machine learning methods are used to carry out searches that depend less on a specific signal model. Experiments such as those at the Large Hadron Collider, neutrino detectors and rare event searches for dark matter, despite having different technical requirements, also share similarities, and there is ground for cooperation to develop machine learning methods. Combining physics and new ideas from statistical learning will be crucial to analysing the large volumes of data to potentially uncover the fundamental structure of nature. There have been large and sustained developments of deep learning in high-energy physics over the past several years. Supervised machine learning methods are widely used to identify known particles and to design targeted searches for specific theories of new physics. Less-than-supervised machine learning methods are used to carry out searches that depend less on a specific signal model. Experiments such as those at the Large Hadron Collider, neutrino detectors and rare event searches for dark matter, despite having different technical requirements, also share similarities, and there is ground for cooperation to develop machine learning methods. Combining physics and new ideas from statistical learning will be crucial to analysing the large volumes of data to potentially uncover the fundamental structure of nature.