Dust formation and mass loss around intermediate-mass AGB stars with initial metallicity Zini≤10−4Z_{\rm ini} \leq 10^{-4} in the early Universe I: Effect of surface opacity on the stellar evolution and dust-driven wind

Dust formation and resulting mass loss around Asymptotic Giant Branch (AGB) stars with initial metallicity in the range of $0 \leq Z_{\rm ini} \leq 10^{-4}$ and initial mass $2\leq M_{\rm ini}/M_{\odot} \leq 5$ are explored by the hydrodynamical calculations of dust-driven wind (DDW) along the AGB evolutionary tracks. We employ the MESA code to simulate the evolution of stars, assuming an empirical mass-loss rate in the post-main sequence phase, and considering the three types of low-temperature opacities (scaled-solar, CO-enhanced, and CNO-enhanced opacities) to elucidate the effect on the stellar evolution and the DDW. We find that the treatment of low-temperature opacity strongly affects the dust formation and resulting DDW; in the carbon-rich AGB phase, the maximum $\dot{M}$ of $M_{\rm ini} \geq$ 3 $M_{\odot}$ star with the CO-enhanced opacity is at least one order of magnitude smaller than that with the CNO-enhanced opacity. A wide range of stellar parameters being covered, a necessary condition for driving efficient DDW with $\dot{M} \ge 10^{-6}$ $M_{\odot}$ yr$^{-1}$ is expressed as the effective temperature $T_{\rm eff} \lesssim 3850$ K and $\log(\delta_{\rm C}L/\kappa_{\rm R} M) \gtrsim 10.43\log T_{\rm eff}-32.33$ with the carbon excess $\delta_{\rm C}$ defined as $\epsilon_{\rm C} - \epsilon_{\rm O}$ and the Rosseland mean opacity $\kappa_{\rm R}$ in units of cm$^2$g$^{-1}$ in the surface layer, and the stellar mass (luminosity) $M$ $(L)$ in solar units. The derived fitting formulae of gas and dust mass-loss rates in terms of input stellar parameters could be useful for investigating the dust yield from AGB stars in the early Universe being consistent with the stellar evolution calculations.Comment: 26 pages, 7 figures, 4 tables, accepted for publication in MNRA

Spatio-Temporal Super-Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks with Domain Generalization

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations or to reduce computational costs. Super-resolution is one such technique for high-resolution inference from low-resolution data. This paper proposes a new scheme, called four-dimensional super-resolution data assimilation (4D-SRDA). This framework calculates the time evolution of a system from low-resolution simulations using a physics-based model, while a trained neural network simultaneously performs data assimilation and spatio-temporal super-resolution. The use of low-resolution simulations without ensemble members reduces the computational cost of obtaining inferences at high spatio-temporal resolution. In 4D-SRDA, physics-based simulations and neural-network inferences are performed alternately, possibly causing a domain shift, i.e., a statistical difference between the training and test data, especially in offline training. Domain shifts can reduce the accuracy of inference. To mitigate this risk, we developed super-resolution mixup (SR-mixup)--a data augmentation method for domain generalization. SR-mixup creates a linear combination of randomly sampled inputs, resulting in synthetic data with a different distribution from the original data. The proposed methods were validated using an idealized barotropic ocean jet with supervised learning. The results suggest that the combination of 4D-SRDA and SR-mixup is effective for robust inference cycles. This study highlights the potential of super-resolution and domain-generalization techniques, in the field of data assimilation, especially for the integration of physics-based and data-driven models

Social Organization and Performance Inequality in Japanese and American Markets

With census data on comparably defined American and Japanese markets, I assess theextent to which competitive advantage is determined by market network structure in thetwo economies. I find significant differences between markets in the two economies, but on average, the social structural parameters known to determine the relative performance of American markets similarly determine the relative performance of Japanese markets. Profit margins are similar on average in corresponding Japanese and American markets, but performance differences between similarly structured American and Japanese markets increase with competitive disadvantage. Being at a competitive disadvantage in Japan is less costly than in the United States.The original publication is available at http://dspace.mit.edu/handle/1721.1/1711

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