Testing the accuracy of halo occupation distribution modelling using hydrodynamic simulations

Beltz-Mohrmann, Gillian D.; Berlind, Andreas A.; Szewciw, Adam O.

doi:10.1093/mnras/stz3442

Cited by 36 publications

(31 citation statements)

References 94 publications

(137 reference statements)

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“…As such, our technique has a stronger physical motivation, as real QSOs are characterised by a high BH accretion rate and strong clustering. Thus, unlike in Sorini et al (2018), our method provides a good match to the observed QSO clustering properties (White et al 2012) without assuming a purely mass-based HOD, which can be simplistic (Beltz-Mohrmann et al 2020;Hadzhiyska et al 2020). At the same time, the number of haloes that we select is the same that we would have selected if we had simply considered all haloes with mass above M min .…”

Section: Selection Of Qsos In Smentioning

confidence: 99%

simba: the average properties of the circumgalactic medium of 2 ≤ z ≤ 3 quasars are determined primarily by stellar feedback

Sorini

Davé

Anglés-Alcázar

2020

Monthly Notices of the Royal Astronomical Society

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We use the Simba cosmological hydrodynamic simulation suite to explore the impact of feedback on the circumgalactic medium (CGM) and intergalactic medium (IGM) around 2 ≤ z ≤ 3 quasars. We identify quasars in Simba as the most rapidly-accreting black holes, and show that they are well-matched in bolometric luminosity and correlation strength to real quasars. We extract Lyα absorption in spectra passing at different transverse distances ($10 \, \mathrm{kpc} \lesssim b \lesssim 10 \: \rm Mpc$) around those quasars, and compare to observations of the mean Lyα absorption profile. The observations are well reproduced, except within $100 \, \: \rm kpc$ from the foreground quasar, where Simba overproduces absorption; this could potentially be mitigated by including ionisation from the quasar itself. By comparing runs with different feedback modules activated, we find that (mechanical) AGN feedback has little impact on the surrounding CGM even around these most highly luminous black holes, while stellar feedback has a significant impact. By further investigating thermodynamic and kinematic properties of CGM gas, we find that stellar feedback, and not AGN feedback, is the primary physical driver in determining the average properties of the CGM around z ∼ 2 − 3 quasars. We also compare our results with previous works, and find that Simba predicts much more absorption within $100 \: \rm kpc$ than the Nyx and Illustris simulations, showing that the Lyα absorption profile can be a powerful constraint on simulations. Instruments such as VLT-MUSE and upcoming surveys (e.g., WEAVE and DESI) promise to further improve such constraints.

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Section: Selection Of Qsos In Smentioning

confidence: 99%

simba: the average properties of the circumgalactic medium of 2 ≤ z ≤ 3 quasars are determined primarily by stellar feedback

Sorini

Davé

Anglés-Alcázar

2020

Monthly Notices of the Royal Astronomical Society

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“…The process of generating multiple mock catalogues in large volumes is very efficient and can thus satisfy the demanding requirements of next-generation experiments. Unfortunately, the simplest versions of such empirical approaches have been shown to make predictions that have significant discrepancies with observations and more detailed galaxy formation models (Croton et al 2007;Paranjape et al 2015;Beltz-Mohrmann et al 2020). For example, several recent works have argued that including secondary HOD parameters, such as the local halo environment and concentration, yields better agreement with hydrodynamical simulations as compared to the standard prescription (Hadzhiyska et al 2020;Xu & Zheng 2020;Hadzhiyska et al 2021a).…”

Section: Introductionmentioning

confidence: 99%

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

Hadzhiyska,

Liu,

Somerville

et al. 2021

Preprint

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In this work, we compare large scale structure observables for stellar mass selected samples at 𝑧 = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo paramaters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy-halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher-order statistics, such as cumulants of the density field, help to accurately describe the galaxy distribution and discriminate between models that show degenerate behavior for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys.

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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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Testing the accuracy of halo occupation distribution modelling using hydrodynamic simulations

Cited by 36 publications

References 94 publications

simba: the average properties of the circumgalactic medium of 2 ≤ z ≤ 3 quasars are determined primarily by stellar feedback

simba: the average properties of the circumgalactic medium of 2 ≤ z ≤ 3 quasars are determined primarily by stellar feedback

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

Mimicking the halo-galaxy connection using machine learning

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