Review Comments for Zeng et al. gmd-2019-310

LeGrand, Sandra L.

doi:10.5194/gmd-2019-310-rc2

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…In this work, we shall investigate this galactic ionizing photon budget. Since star formation efficiency typically rises with halo mass within the range of masses we represent (not only in MNRAS 496, 4342-4357 (2020) CoDa II, but as generally expected, see Moster, Naab & White 2013;Legrand et al 2019), while the abundance of haloes decreases with halo mass (e.g. see the halo mass functions of Sheth, Mo & Tormen 2001;Watsonetal.2013), the mass range of contributing haloes may, in principle, be broad, with a maximum contribution from haloes that, at different redshifts, may be anywhere within the broad range 10 8 M ⊙ < M halo < 10 12 M ⊙ .…”

Section: Introductionsupporting

confidence: 61%

Galactic ionizing photon budget during the epoch of reionization in the Cosmic Dawn II simulation

Lewis

Ocvirk

Aubert

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Cosmic Dawn II yields the first statistically meaningful determination of the relative contribution to reionization by galaxies of different halo mass, from a fully coupled radiation-hydrodynamics simulation of the epoch of reionization large enough (∼100 Mpc) to model global reionization while resolving the formation of all galactic haloes above ${\sim}10^8 \, {\rm M}_{\odot }$. Cell transmission inside haloes is bi-modal – ionized cells are transparent, while neutral cells absorb the photons their stars produce – and the halo escape fraction fesc reflects the balance of star formation rate (SFR) between these modes. The latter is increasingly prevalent at higher halo mass, driving down fesc (we provide analytical fits to our results), whereas halo escape luminosity, proportional to fesc × SFR, increases with mass. Haloes with dark matter masses within $6\times 10^{8} \, {\rm M}_{\odot }\lt M_{\rm halo}\lt 3 \times 10^{10} \, {\rm M}_{\odot }$ produce ∼80 per cent of the escaping photons at z = 7, when the universe is 50 per cent ionized, making them the main drivers of cosmic reionization. Less massive haloes, though more numerous, have low SFRs and contribute less than 10 per cent of the photon budget then, despite their high fesc. High-mass haloes are too few and too opaque, contributing <10 per cent despite their high SFRs. The dominant mass range is lower (higher) at higher (lower) redshift, as mass function and reionization advance together (e.g. at z = 8.5, xH i = 0.9, $M_{\rm halo}\lt 5\times 10^9 \, {\rm M}_{\odot }$ haloes contributed ∼80 per cent). Galaxies with UV magnitudes MAB1600 between −12 and −19 dominated reionization between z = 6 and 8.

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Section: Introductionsupporting

confidence: 61%

Galactic ionizing photon budget during the epoch of reionization in the Cosmic Dawn II simulation

Lewis

Ocvirk

Aubert

et al. 2020

Monthly Notices of the Royal Astronomical Society

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“…Such models have been used to constrain the stellar mass -halo mass relation (e.g. Behroozi et al 2010;Moster et al 2010Moster et al , 2013Legrand et al 2019), though it has been noted that the efficacy of such methods is highly dependent on the observational selection function (Stiskalek et al 2021). Both these approaches are capable of modelling galaxy evolution over very large volumes, allowing predictions for the clustering of galaxies as well as their evolution in rare, overdense environments.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to mapping baryons on to dark matter haloes using the eagle and C-EAGLE simulations

Lovell

Wilkins

Thomas

et al. 2021

Monthly Notices of the Royal Astronomical Society

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High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure clustering statistics of the large scale structure. Typically, zoom simulations of individual regions are used to study rare environments, and semi-analytic models and halo occupation models applied to dark matter only (DMO) simulations are used to study the Universe in the large-volume regime. We propose a new approach, using a machine learning framework to explore the halo-galaxy relationship in the periodic eagle simulations, and zoom C-EAGLE simulations of galaxy clusters. We train a tree based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. The trained model successfully reproduces a number of key distribution functions for an infinitesimal fraction of the computational cost of a full hydrodynamic simulation. By training on both periodic simulations as well as zooms of overdense environments, we learn the bias of galaxy evolution in differing environments. This allows us to apply the trained model to a larger DMO volume than would be possible if we only trained on a periodic simulation. We demonstrate this application using the (800 Mpc)3 P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the eagle model in this large volume.

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Review Comments for Zeng et al. gmd-2019-310

Cited by 2 publications

References 13 publications

Galactic ionizing photon budget during the epoch of reionization in the Cosmic Dawn II simulation

Galactic ionizing photon budget during the epoch of reionization in the Cosmic Dawn II simulation

A machine learning approach to mapping baryons on to dark matter haloes using the eagle and C-EAGLE simulations

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