The Halo Void (Dust) Model of large scale structure

Voivodic, Rodrigo; Rubira, Henrique; Lima, M.

doi:10.1088/1475-7516/2020/10/033

Cited by 21 publications

(32 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This result follows previous works (e.g. Massara et al 2014;Voivodic et al 2020) but narrows down the source of uncertainties by using the halo mass function and density profiles directly from the simulations.…”

Section: Discussionsupporting

confidence: 88%

“…to 15%. This is qualitatively consistent with previous findings (e.g., Giocoli et al 2010;Massara et al 2014;Mead et al 2015;Chen & Afshordi 2020;Voivodic et al 2020), although note that our test is more stringent due to the fact that we are using the same simulation to inform and then test the halo model. At small scales we see that the level of agreement improves again (< 5% at 1 < 𝑘 [ℎ/Mpc] < 4) between the BAHAMAS-informed halo model predictions and the simulations and theoretical predictions.…”

Section: Collisionless Matter Power Spectrumsupporting

confidence: 92%

See 1 more Smart Citation

The BAHAMAS project: evaluating the accuracy of the halo model in predicting the non-linear matter power spectrum

Acuto

McCarthy

Kwan

et al. 2021

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

The halo model formalism is widely adopted in cosmological studies for predicting the growth of large-scale structure in the Universe. However, to date there have been relatively few direct comparisons of the halo model with more accurate (but much more computationally expensive) cosmological simulations. We test the accuracy of the halo model in reproducing the non-linear matter power spectrum, P(k), when the main inputs of the halo model (specifically the matter density profiles, halo mass function, and linear bias) are taken directly from the BAHAMAS simulations and we assess how well the halo model reproduces P(k) from the same simulations. We show that the halo model generally reproduces P(k) in the deep non-linear regime (1-halo) to typically a few percent accuracy, but struggles to reproduce (approx. 15 per cent error) P(k) at intermediate scales of $0.1 \lesssim k \ [h/{\rm Mpc}] \lesssim 3$ at z = 0, marking the transition between the 1-halo and 2-halo terms. We show that the magnitude of this error is a strong function of the halo mass definition (through its effects on radial extent of haloes) and of redshift. Furthermore, we test the accuracy of the halo model in recovering the relative impact of baryons on P(k). We show that the systematic errors in recovering the absolute P(k) largely cancel when considering the relative impact of baryons. This suggests that the halo model can make precise predictions for the baryonic suppression, offering a fast and accurate way to adjust collisionless matter power spectra for the presence of baryons and associated processes.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Collisionless Matter Power Spectrumsupporting

confidence: 92%

The BAHAMAS project: evaluating the accuracy of the halo model in predicting the non-linear matter power spectrum

Acuto

McCarthy

Kwan

et al. 2021

Monthly Notices of the Royal Astronomical Society

View full text Add to dashboard Cite

show abstract

“…The low-k pass filter, S N (k), is necessary since in this work we focus on void clustering at large scales, as, in this case, the behaviour of the void power spectrum, P vv (k, z), is well described by linear theory via a simple multiplicative factor for the void bias. This can be understood in the context of the halo model formalism (Cooray & Sheth 2002) which has an analog formulation for voids (Voivodic et al 2020). Such an assumption is inaccurate at small scales (not considered in the present work), which are sensitive to the void density profile (Hamaus et al 2014b).…”

Section: The Clustering Of Cosmic Voidsmentioning

confidence: 93%

Euclid: Forecasts from the void-lensing cross-correlation

Bonici,

Carbone,

Davini

et al. 2022

Preprint

View full text Add to dashboard Cite

The Euclid space telescope will survey a large dataset of cosmic voids traced by dense samples of galaxies. In this work we estimate its expected performance when exploiting angular photometric void clustering, galaxy weak lensing and their cross-correlation. To this aim, we implement a Fisher matrix approach tailored for voids from the Euclid photometric dataset and present the first forecasts on cosmological parameters that include the void-lensing correlation. We examine two different probe settings, pessimistic and optimistic, both for void clustering and galaxy lensing. We carry out forecast analyses in four model cosmologies, accounting for a varying total neutrino mass, M ν , and a dynamical dark energy (DE) equation of state, w(z), described by the popular Chevallier-Polarski-Linder parametrisation. We find that void clustering constraints on h and Ω b are competitive with galaxy lensing alone, while errors on n s decrease thanks to the orthogonality of the two probes in the 2D-projected parameter space. We also note that, as a whole, with respect to assuming the two probes as independent, the inclusion of the void-lensing crosscorrelation signal improves parameter constraints by 10 − 15%, and enhances the joint void clustering and galaxy lensing Figure of Merit (FoM) by 10% and 25%, in the pessimistic and optimistic scenarios, respectively. Finally, when further combining with the spectroscopic galaxy clustering, assumed as an independent probe, we find that, in the most competitive case, the FoM increases by a factor of 4 with respect to the combination of weak lensing and spectroscopic galaxy clustering taken as independent probes. The forecasts presented in this work show that photometric void-clustering and its cross-correlation with galaxy lensing deserve to be exploited in the data analysis of the Euclid galaxy survey and promise to improve its constraining power, especially on h, Ω b , the neutrino mass, and the DE evolution.

show abstract

“…It is worth examining some of the approximations that lead to the standard halo-model equations ( 8) and ( 11): It has been assumed that halo profiles are perfectly spherical with no substructure, that there is no scatter in profile properties at fixed host halo mass, and that halo properties depend only on halo mass and not on other properties, for example halo location. These approximations will break down, and the errors in the eventual power spectrum that they contribute will vary with the fields that are being considered (e.g., Smith & Watts 2005;Giocoli et al 2010;Smith & Markovic 2011;Chen & Afshordi 2019;Voivodic et al 2020). It is also usually assumed that haloes trace the underlying linear matter distribution with a linear halo bias.…”

Section: Including Non-linear Halo Biasmentioning

confidence: 99%

Including beyond-linear halo bias in halo models

Mead,

Verde

2020

Preprint

View full text Add to dashboard Cite

We derive a simple prescription for including beyond-linear halo bias within the standard, analytical halo-model power spectrum calculation. This results in a corrective term that is added to the usual two-halo term. We measure this correction using data from N-body simulations and demonstrate that it can boost power in the two-halo term by a factor of ∼ 2 at scales k ∼ 0.7 hMpc −1 , with the exact magnitude of the boost determined by the specific pair of fields in the two-point function. How this translates to the full power spectrum depends on the relative strength of the one-halo term, which can mask the importance of this correction to a greater or lesser degree, again depending on the fields. Generally we find that our correction is more important for signals that arise from lower-mass haloes. When comparing our calculation to simulated data we find that the under-prediction of power in the transition region between the two-and one-halo terms, which typically plagues halo-model calculations, is almost completely eliminated when including the full non-linear halo bias. We show improved results for the auto and cross spectra of galaxies, haloes and matter. In the specific case of matter-matter or matter-halo power we note that a large fraction of the improvement comes from the non-linear biasing between low-and high-mass haloes. We envisage our model being useful in the analytical modelling of cross correlation signals. Our non-linear bias halo-model code is available at https://github.com/alexander-mead/BNL.

show abstract

The Halo Void (Dust) Model of large scale structure

Cited by 21 publications

References 105 publications

The BAHAMAS project: evaluating the accuracy of the halo model in predicting the non-linear matter power spectrum

The BAHAMAS project: evaluating the accuracy of the halo model in predicting the non-linear matter power spectrum

Euclid: Forecasts from the void-lensing cross-correlation

Including beyond-linear halo bias in halo models

Contact Info

Product

Resources

About