The standard Dynes-Fulton reconstruction of critical current density breaks down for junctions with nonlinear phase distributions, and the authors propose an iterative algorithm that incorporates prior knowledge to fix it.
r-process Abundance Patterns in the Globular Cluster M92
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abstract
Whereas light element abundance variations are a hallmark of globular clusters, there is little evidence for variation in neutron-capture elements. A significant exception is M15, which shows a star-to-star dispersion in neutron-capture abundances of at least one order of magnitude. The literature contains evidence both for and against a neutron-capture dispersion in M92. We conducted an analysis of archival Keck/HIRES spectra of 35 stars in M92, 29 of which are giants, which we use exclusively for our conclusions. M92 conforms to the light element abundance variations typical of massive clusters. Like other globular clusters, its neutron-capture abundances were generated by the r-process. We confirm a star-to-star dispersion in the r-process. Unlike M15, the dispersion is limited to "first-generation" (low Na, high Mg) stars, and the dispersion is smaller for Sr, Y, and Zr than for Ba and the lanthanides. This is the first detection of a relation between light element and neutron-capture abundances in a globular cluster. We propose that a source of the main r-process polluted the cluster shortly before or concurrently with the first generation of star formation. The heavier r-process abundances were inhomogeneously distributed while the first-generation stars were forming. The second-generation stars formed after several crossing times (~0.8 Myr); hence, the second generation shows no r-process dispersion. This scenario imposes a minimum temporal separation of 0.8 Myr between the first and second generations.
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Reconstructing Critical Current Density in Josephson Junctions with Phase Non-linearity
The standard Dynes-Fulton reconstruction of critical current density breaks down for junctions with nonlinear phase distributions, and the authors propose an iterative algorithm that incorporates prior knowledge to fix it.