Structure and magnetic properties of nanostructured Dy/transition-metal multilayered films

We report the results of magnetic and microstructural studies for T/Dy (T=Fe, Co, Ni) compositionally modulated films prepared in a multiple-gun sputtering system. The perpendicular anisotropy and magnetization were measured systematically for X-Å Fe/Y-Å Dy and X-Å Co/Y-Å Dy films. The layer-thickness dependence of the magnetization for Co/Dy and Fe/Dy was interpreted in terms of the antiparallel coupling between transition-metal and Dy magnetic moments. For Co/Dy films the ranges of X and Y required for perpendicular anisotropy were determined. A comparision of the structural and magnetic properties of Ni/Dy, Co/Dy, and Fe/Dy is given and the origin of the perpendicular anisotropy is discussed. Journal of Applied Physics is copyrighted by The American Institute of Physics

Magnetic properties, anisotropy, and microstructure of sputtered rare-earth iron multilayers

A study of compositionally modulated magnetic films of the form Fe/RE, particularly for RE=Nd and Dy, has been performed by vibrating sample magnetometry, ac susceptibility and x-ray diffraction. The relationship between the magnetic properties and the layer thickness was studied systematically for X-Å Fe/Y-Å Dy, as the layer thicknesses X and Y were varied from 1.8 to 20 Å. The ranges of layer thicknesses required for perpendicular anisotropy were determined. The interface and volume anisotropy energies were estimated for X-Å Fe/Y-Å Nd and the differences in the magnetic properties between X-Å Fe/7-Å Dy and X-Å Fe/7-Å Nd are discussed. Journal of Applied Physics is copyrighted by The American Institute of Physics

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE (CAPE) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.</p

The entanglement in one-dimensional random XY spin chain with Dzyaloshinskii-Moriya interaction

The impurities of exchange couplings, external magnetic fields and Dzyaloshinskii--Moriya (DM) interaction considered as Gaussian distribution, the entanglement in one-dimensional random $XY$ spin systems is investigated by the method of solving the different spin-spin correlation functions and the average magnetization per spin. The entanglement dynamics at central locations of ferromagnetic and antiferromagnetic chains have been studied by varying the three impurities and the strength of DM interaction. (i) For ferromagnetic spin chain, the weak DM interaction can improve the amount of entanglement to a large value, and the impurities have the opposite effect on the entanglement below and above critical DM interaction. (ii) For antiferromagnetic spin chain, DM interaction can enhance the entanglement to a steady value. Our results imply that DM interaction strength, the impurity and exchange couplings (or magnetic field) play competing roles in enhancing quantum entanglement.Comment: 12 pages, 3 figure

A conjecture on the origin of dark energy

The physical origin of holographic dark energy (HDE) is investigated. The main existing explanations, namely the UV/IR connection argument of Cohen et al, Thomas' bulk holography argument, and Ng's spacetime foam argument, are shown to be not satisfactory. A new explanation of the HDE model is then proposed based on the ideas of Thomas and Ng. It is suggested that the dark energy might originate from the quantum fluctuations of spacetime limited by the event horizon of the universe. Several potential problems of the explanation are also discussed.Comment: 11 pages, no figure

Weak-lensing calibration of a stellar mass-based mass proxy for redMaPPer and Voronoi Tessellation clusters in SDSS Stripe 82

We present the first weak lensing calibration of $\mu_{\star}$, a new galaxy cluster mass proxy corresponding to the total stellar mass of red and blue members, in two cluster samples selected from the SDSS Stripe 82 data: 230 redMaPPer clusters at redshift $0.1\leq z<0.33$ and 136 Voronoi Tessellation (VT) clusters at $0.1 \leq z < 0.6$. We use the CS82 shear catalog and stack the clusters in $\mu_{\star}$ bins to measure a mass-observable power law relation. For redMaPPer clusters we obtain $M_0 = (1.77 \pm 0.36) \times 10^{14}h^{-1} M_{\odot}$, $\alpha = 1.74 \pm 0.62$. For VT clusters, we find $M_0 = (4.31 \pm 0.89) \times 10^{14}h^{-1} M_{\odot}$, $\alpha = 0.59 \pm 0.54$ and $M_0 = (3.67 \pm 0.56) \times 10^{14}h^{-1} M_{\odot}$, $\alpha = 0.68 \pm 0.49$ for a low and a high redshift bin, respectively. Our results are consistent, internally and with the literature, indicating that our method can be applied to any cluster finding algorithm. In particular, we recommend that $\mu_{\star}$ be used as the mass proxy for VT clusters. Catalogs including $\mu_{\star}$ measurements will enable its use in studies of galaxy evolution in clusters and cluster cosmology.Comment: Updated to be consistent with the published versio

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

The global economy is facing a serious recession due to COVID-19, with implications for CO2 emissions. Here, using a global adaptive multiregional input–output model and scenarios of lockdown and fiscal counter measures, we show that global emissions from economic sectors will decrease by 3.9 to 5.6% in 5 years (2020 to 2024) compared with a no-pandemic baseline scenario (business as usual for economic growth and carbon intensity decline). Global economic interdependency via supply chains means that blocking one country’s economic activities causes the emissions of other countries to decrease even without lockdown policies. Supply-chain effects contributed 90.1% of emissions decline from power production in 2020 but only 13.6% of transport sector reductions. Simulations of follow-up fiscal stimuli in 41 major countries increase global 5-yr emissions by −6.6 to 23.2 Gt (−4.7 to 16.4%), depending on the strength and structure of incentives. Therefore, smart policy is needed to turn pandemic-related emission declines into firm climate action

Magnetic hardening in FePt nanostructured films

FePt films have been prepared by sputtering Fe/Pt multilayers onto glass or silicon substrates. The thickness of the Fe and Pt layers was adjusted with the Fe:Pt atomic ratio from about 1:1 to 2:1. Magnetic hardening is observed after heat treatment at elevated temperatures, which led to coercivity values exceeding 20 kOe in samples with an Fe:Pt ratio around 1.2:1. The hardening originates from the formation of the tetragonal FePt phase with high magnetocrystalline anisotropy and a favorable microstructure. Two-phase composite films containing hard and soft phases were obtained when the Fe:Pt ratio increased. Under optimized processing conditions, composite films with energy products larger than 30 MG Oe at room temperature have been successfully produced