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      Evidence from Disrupted Halo Dwarfs that r-process Enrichment via Neutron Star Mergers is Delayed by ≳500 Myr

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          Abstract

          The astrophysical origins of r-process elements remain elusive. Neutron star mergers (NSMs) and special classes of core-collapse supernovae (rCCSNe) are leading candidates. Due to these channels’ distinct characteristic timescales (rCCSNe: prompt, NSMs: delayed), measuring r-process enrichment in galaxies of similar mass but differing star formation durations might prove informative. Two recently discovered disrupted dwarfs in the Milky Way’s stellar halo, Kraken and Gaia-Sausage Enceladus (GSE), afford precisely this opportunity: Both have M ≈ 10 8 M but differing star formation durations of ≈2 Gyr and ≈3.6 Gyr. Here we present R ≈ 50,000 Magellan/MIKE spectroscopy for 31 stars from these systems, detecting the r-process element Eu in all stars. Stars from both systems have similar [Mg/H] ≈ −1, but Kraken has a median [Eu/Mg] ≈ −0.1 while GSE has an elevated [Eu/Mg] ≈ 0.2. With simple models, we argue NSM enrichment must be delayed by 500–1000 Myr to produce this difference. rCCSNe must also contribute, especially at early epochs, otherwise stars formed during the delay period would be Eu free. In this picture, rCCSNe account for ≈50% of the Eu in Kraken, ≈25% in GSE, and ≈15% in dwarfs with extended star formation durations like Sagittarius. The inferred delay time for NSM enrichment is 10×–100× longer than merger delay times from stellar population synthesis—this is not necessarily surprising because the enrichment delay includes time taken for NSM ejecta to be incorporated into subsequent generations of stars. For example, this may be due to natal kicks that result in r-enriched material deposited far from star-forming gas, which then takes ≈10 8–10 9 yr to cool in these galaxies.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Matplotlib: A 2D Graphics Environment

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                Journal
                The Astrophysical Journal Letters
                ApJL
                American Astronomical Society
                2041-8205
                2041-8213
                February 24 2022
                February 01 2022
                February 24 2022
                February 01 2022
                : 926
                : 2
                : L36
                Article
                10.3847/2041-8213/ac5589
                daf58a40-5d8f-4e39-81a2-3e4bfb3395ef
                © 2022

                http://creativecommons.org/licenses/by/4.0/

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