The Formation History of Subhalos and the Evolution of Satellite Galaxies

Shi, Jingjing; Wang, Huiyuan; Mo, H. J.; Vogelsberger, Mark; Ho, Luis C.; Du, Min; Nelson, Dylan; Pillepich, Annalisa; Hernquist, Lars

doi:10.3847/1538-4357/ab8464

Cited by 24 publications

(15 citation statements)

References 128 publications

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“…However, GSE is thought to have formed close to the MW and was accreted at early times (e.g., Gallart et al 2019;Mackereth et al 2019), Sgr and Fnx at more intermediate times (e.g., Rocha et al 2012;Fillingham et al 2019), and the MCs are only falling in recently, having likely evolved in near isolation until now (e.g., Besla et al 2016). In the paradigm of Gallart et al (2015), in which dwarfs galaxies are assigned to two groups according to their SFH, we could consider the MCs to be "slow dwarfs", forming the bulk of their stars more recently, as compared to the "fast dwarfs" (e.g., Sgr, GSE, and Fnx), that started their evolution with a dominant early star formation event (see e.g., Shi et al 2020 for similar simulation results). However, even the MCs became "faster" dwarfs when they began to interact with each other driving up the α-element abundances.…”

Section: Mass and Environmental Effects On Galaxymentioning

confidence: 99%

APOGEE Chemical Abundance Patterns of the Massive Milky Way Satellites

Hasselquist,

Hayes,

Lian

et al. 2021

Preprint

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The SDSS-IV Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey has obtained high-resolution spectra for thousands of red giant stars distributed among the massive satellite galaxies of the Milky Way (MW): the Large and Small Magellanic Clouds (LMC/SMC), the Sagittarius Dwarf (Sgr), Fornax (Fnx), and the now fully disrupted Gaia Sausage/Enceladus (GSE) system. We present and analyze the APOGEE chemical abundance patterns of each galaxy to draw robust conclusions about their star formation histories, by quantifying the relative abundance trends of multiple elements (C, N, O, Mg, Al, Si, Ca, Fe, Ni, and Ce), as well as by fitting chemical evolution models to the [α/Fe]-[Fe/H] abundance plane for each galaxy. Results show that the chemical signatures of the starburst in the MCs observed by Nidever et al. in the α-element abundances extend to C+N, Al, and Ni, with the major burst in the SMC occurring some 3-4 Gyr before the burst in the LMC. We find that Sgr and Fnx also exhibit chemical abundance patterns suggestive of secondary star formation epochs, but these events were weaker and earlier (∼ 5-7 Gyr ago) than those observed in the MCs. There is no chemical evidence of a second starburst in GSE, but this galaxy shows the strongest initial star formation as compared to the other four galaxies. All dwarf galaxies had greater relative contributions of AGB stars to their enrichment than the MW. Comparing and contrasting these chemical patterns highlight the importance of galaxy environment on its chemical evolution.

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Section: Mass and Environmental Effects On Galaxymentioning

confidence: 99%

APOGEE Chemical Abundance Patterns of the Massive Milky Way Satellites

Hasselquist,

Hayes,

Lian

et al. 2021

Preprint

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“…The TNG300 run followed the dynamical evolution of 2500 3 dark matter (DM) particles of mass 4.0 × 10 7 ℎ −1 M and (initially) 2500 3 gas cells of mass 7.6 × 10 6 ℎ −1 M . This box is a useful tool for galaxy formation and clustering science that has proven capable of reproducing a number of observational measurements (see, e.g., Springel et al 2018;Pillepich et al 2018a;Bose et al 2019;Beltz-Mohrmann et al 2020;Contreras et al 2020a;Gu et al 2020;Hadzhiyska et al 2020b;Montero-Dorta et al 2020b;Shi et al 2020;Hadzhiyska et al 2021;Montero-Dorta et al 2021a,b;Favole et al 2021).…”

Section: The Illustris-tng Datamentioning

confidence: 99%

Mimicking the halo-galaxy connection using machine learning

de Santi,

Rodrigues,

Montero-Dorta

et al. 2022

Preprint

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Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in theories of galaxy formation. When the spatial distribution of objects is also taken under consideration, investigating the halo-galaxy connection becomes very relevant for cosmological measurements. In this paper, we use machine learning (ML) techniques to analyze these intricate relations in the IllustrisTNG300 magnetohydrodynamical simulation. We employ four different algorithms: extremely randomized trees (ERT), K-nearest neighbors (kNN), light gradient boosting machine (LGBM), and neural networks (NN), along with a stacked model where we combine results from all four approaches. Overall, the different ML algorithms produce consistent results in terms of predicting galaxy properties from a set of input halo properties that include halo mass, concentration, spin, and halo overdensity. For stellar mass, the (predicted v. true) Pearson correlation coefficient is 0.98, dropping down to 0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition, we test an existing data augmentation technique, designed to alleviate the problem of unbalanced datasets, and show that it improves slightly the shape of the predicted distributions. We also demonstrate that our predictions are good enough to reproduce the power spectra of multiple galaxy populations, defined in terms of stellar mass, sSFR, colour, and size with high accuracy. Our results align with previous reports suggesting that certain galaxy properties cannot be reproduced using halo features alone.

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“…The TNG300 and TNG300-DMO simulations have been already employed to investigate several features on the galaxy-dark matter halo connection (Bose et al 2019;Contreras et al 2020a;Gu et al 2020;Hadzhiyska et al 2020b,a;Shi et al 2020;Montero-Dorta et al 2020b,a) proving they are a suitable tool for the goal of this paper. In the next section we describe the set of properties implemented.…”

Section: Simulation Datamentioning

confidence: 99%

SHAM through the lens of a hydrodynamical simulation

Favole,

Montero-Dorta,

Artale

et al. 2021

Preprint

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We use the IllustrisTNG300 hydrodynamical simulation to study the dependence of the galaxy two-point correlation function on a broad range of secondary halo and galactic properties. We construct galaxy mock catalogues using a standard sub-halo abundance matching scheme coupled with a secondary assignment between galaxy colour or specific star formation rate and the following halo properties: starvation redshift z starve , concentration at infall, dark matter density contrast 𝛿 env 𝑅 , tidal anisotropy 𝛼 𝑅 , and tidal overdensity 𝛿 𝑅 . The last two quantities allow us to fully characterise the tidal field of our haloes, acting as mediators between their internal and large-scale properties. The resulting mock catalogues return different levels of agreement with the IllustrisTNG300 measurements and strongly depend on the secondary halo property employed. Among all the secondary halo properties tested, we find that z starve and 𝛿 𝑅 are the ones that best trace the large-scale structure, producing reliable clustering predictions for different samples of red/blue and quenched/star-forming galaxies.

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The Formation History of Subhalos and the Evolution of Satellite Galaxies

Cited by 24 publications

References 128 publications

APOGEE Chemical Abundance Patterns of the Massive Milky Way Satellites

APOGEE Chemical Abundance Patterns of the Massive Milky Way Satellites

Mimicking the halo-galaxy connection using machine learning

SHAM through the lens of a hydrodynamical simulation

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