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      VINTERGATAN-GM: The cosmological imprints of early mergers on Milky-Way-mass galaxies

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          ABSTRACT

          We present a new suite of cosmological zoom-in hydrodynamical ($\approx 20\, \mathrm{pc}$ spatial resolution) simulations of Milky-Way mass galaxies to study how a varying mass ratio for a Gaia-Sausage-Enceladus (GSE) progenitor impacts the z = 0 chemodynamics of halo stars. Using the genetic modification approach, we create five cosmological histories for a Milky-Way-mass dark matter halo ($M_{200}\approx 10^{12} \, \mbox{M}_\mathrm{\odot }$), incrementally increasing the stellar mass ratio of a z ≈ 2 merger from 1:25 to 1:2, while fixing the galaxy’s final dynamical, stellar mass, and large-scale environment. We find markedly different morphologies at z = 0 following this change in early history, with a growing merger resulting in increasingly compact and bulge-dominated galaxies. Despite this structural diversity, all galaxies show a radially biased population of inner halo stars like the Milky-Way’s GSE which, surprisingly, has a similar magnitude, age, $\rm [Fe/H]$, and $\rm [\alpha /Fe]\(distribution whether the z ≈ 2 merger is more minor or major. This arises because a smaller ex-situ population at z ≈ 2 is compensated by a larger population formed in an earlier merger-driven starburst whose contribution to the GES can grow dynamically over time, and with both populations strongly overlapping in the \)\rm [Fe/H]-\rm [\alpha /Fe]$ plane. Our study demonstrates that multiple high-redshift histories can lead to similar z = 0 chemodynamical features in the halo, highlighting the need for additional constraints to distinguish them, and the importance of considering the full spectrum of progenitors when interpreting z = 0 data to reconstruct our Galaxy’s past.

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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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              The NumPy Array: A Structure for Efficient Numerical Computation

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                Author and article information

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                Journal
                Monthly Notices of the Royal Astronomical Society
                Oxford University Press (OUP)
                0035-8711
                1365-2966
                May 2023
                March 02 2023
                May 2023
                March 02 2023
                February 15 2023
                : 521
                : 1
                : 995-1012
                Article
                10.1093/mnras/stad513
                379f8353-6735-4181-97a9-019675ddc484
                © 2023

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

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