Examinando por Autor "Marinacci, Federico"

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    Machine learning for galactic archaeology: a chemistry-based neural network method for identification of accreted disc stars
    (Oxford University Press, 2022-09) Tronrud, Thorold; Tissera, Patricia B.; Gómez, Facundo A.; Grand, Robert J. J.; Pakmor, Ruediger; Marinacci, Federico; Simpson, Christine M.
    We develop a method ('Galactic Archaeology Neural Network', gann) based on neural network models (NNMs) to identify accreted stars in galactic discs by only their chemical fingerprint and age, using a suite of simulated galaxies from the Auriga Project. We train the network on the target galaxy's own local environment defined by the stellar halo and the surviving satellites. We demonstrate that this approach allows the detection of accreted stars that are spatially mixed into the disc. Two performance measures are defined - recovery fraction of accreted stars, frecov and the probability that a star with a positive (accreted) classification is a true-positive result, P(TP). As the NNM output is akin to an assigned probability (Pa), we are able to determine positivity based on flexible threshold values that can be adjusted easily to refine the selection of presumed-accreted stars. We find that gann identifies accreted disc stars within simulated galaxies, with high frecov and/or high P(TP). We also find that stars in Gaia-Enceladus-Sausage (GES) mass systems are over 50 per cent recovered by our NNMs in the majority (18/24) of cases. Additionally, nearly every individual source of accreted stars is detected at 10 per cent or more of its peak stellar mass in the disc. We also demonstrate that a conglomerated NNM, trained on the halo and satellite stars from all of the Auriga galaxies provides the most consistent results, and could prove to be an intriguing future approach as our observational capabilities expand. © 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
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    The Auriga stellar haloes: Connecting stellar population properties with accretion and merging history
    (Monthly Notices of the Royal Astronomical Society, 2019-02-15) Monachesi, Antonela; Gomez, Facundo A.; Grand, Robert J. J.; Simpson, Christine M.; Kauffmann, Guinevere; Bustamante, Sebastian; Marinacci, Federico; Pakmor, Rudiger; Springel, Volker; Frenk, Carlos S.; White, Simon D. M.; Tissera, Patricia B.
    We examine the stellar haloes of the Auriga simulations, a suite of 30 cosmological magnetohydrodynamical high-resolution simulations of Milky Way-mass galaxies performed with the moving-mesh code AREPO. We study halo global properties and radial profiles out to ∼150 kpc for each individual galaxy. The Auriga haloes are diverse in their masses and density profiles, mean metallicity and metallicity gradients, ages, and shapes, reflecting the stochasticity inherent in their accretion and merger histories. A comparison with observations of nearby late-type galaxies shows very good agreement between most observed and simulated halo properties. However, Auriga haloes are typically too massive. We find a connection between population gradients and mass assembly history: galaxies with few significant progenitors have more massive haloes, possess large negative halo metallicity gradients, and steeper density profiles. The number of accreted galaxies, either disrupted or under disruption, that contribute 90 per cent of the accreted halo mass ranges from 1 to 14, with a median of 6.5, and their stellar masses span over three orders of magnitude. The observed halo mass-metallicity relation is well reproduced by Auriga and is set by the stellar mass and metallicity of the dominant satellite contributors. This relationship is found not only for the accreted component but also for the total (accreted + in situ) stellar halo. Our results highlight the potential of observable halo properties to infer the assembly history of galaxies.