Scatter in Sunyaev–Zel’dovich effect scaling relations explained by inter-cluster variance in mass accretion histories

Green, Sheridan B.; Aung, Han; Nagai, Daisuke; Bosch, Frank C. van den

doi:10.1093/mnras/staa1712

Cited by 20 publications

(16 citation statements)

References 160 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent progress in modeling subhalo orbital evolution has demonstrated the importance of accounting for the evolution of the host halo potential (Ogiya et al 2021), and so deploying our model in this context could be leveraged to capture physically realistic accretion-rate correlations in the halo-to-halo variance of substructure abundance and orbits across cosmic time (see, e.g., Jiang & van den Bosch 2017). Along similar lines, it has recently been shown that scatter in the thermal Sunyaev-Zel'dovich effect is largely driven by variance in the assembly histories of cluster-mass halos (Green et al 2020), and so our model may also help improve the predictive power of models for the massobservable relation of galaxy clusters.…”

Section: Discussion and Future Worksupporting

confidence: 53%

A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos

Hearin¹,

Chaves-Montero²,

Becker³

et al. 2021

TheOJA

View full text Add to dashboard Cite

We present a new empirical model for the mass assembly of dark matter halos. We approximate the growth of individual halos as a simple power-law function of time, where the power-law index smoothly decreases as the halo transitions from the fast-accretion regime at early times, to the slow-accretion regime at late times. Using large samples of halo merger trees taken from high-resolution cosmological simulations, we demonstrate that our 3-parameter model can approximate halo growth with a typical accuracy of 0.1 dex for t 1 Gyr for all halos of present-day mass M halo 10 11 M , including subhalos and host halos in gravity-only simulations, as well as in the IllustrisTNG hydrodynamical simulation. We additionally present a new model for the assembly of halo populations, which not only reproduces average mass growth across time, but also faithfully captures the diversity with which halos assemble their mass. Our python implementation is based on the autodiff library JAX, and so our model selfconsistently captures the mean and variance of halo mass accretion rate across cosmic time. We show that the connection between halo assembly and the large-scale density field, known as halo assembly bias, is accurately captured by our model, and that residual errors in our approximations to halo assembly history exhibit a negligible residual correlation with the density field. Our publicly available source code can be used to generate Monte Carlo realizations of cosmologically representative halo histories; our differentiable implementation facilitates the incorporation of our model into existing analytical halo model frameworks.

show abstract

Section: Discussion and Future Worksupporting

confidence: 53%

A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos

Hearin¹,

Chaves-Montero²,

Becker³

et al. 2021

TheOJA

View full text Add to dashboard Cite

show abstract

“…As discussed in detail in , this halo-tohalo variance is predominately driven by variance in the halo mass accretion histories (see also e.g. Giocoli et al 2010;Green et al 2020). The second-order f sub also increases with M 0 , has much larger log-scatter than the total f sub , and its mean is smaller by a factor of ≈15−30.…”

Section: Mass-dependence and Halo-to-halo Variance Of F Submentioning

confidence: 94%

The tidal evolution of dark matter substructure – II. The impact of artificial disruption on subhalo mass functions and radial profiles

Green

Bosch

Jiang

2021

Monthly Notices of the Royal Astronomical Society

Self Cite

View full text Add to dashboard Cite

Several recent studies have indicated that artificial subhalo disruption (the spontaneous, non-physical disintegration of a subhalo) remains prevalent in state-of-the-art dark matter-only cosmological simulations. In order to quantify the impact of disruption on the inferred subhalo demographics, we augment the semi-analytical SatGen dynamical subhalo evolution model with an improved treatment of tidal stripping that is calibrated using the DASH database of idealized high-resolution simulations of subhalo evolution, which are free from artificial disruption. We also develop a model of artificial disruption that reproduces the statistical properties of disruption in the Bolshoi simulation. Using this framework, we predict subhalo mass functions (SHMFs), number density profiles, and substructure mass fractions and study how these quantities are impacted by artificial disruption and mass resolution limits. We find that artificial disruption affects these quantities at the $10-20\%$ level, ameliorating previous concerns that it may suppress the SHMF by as much as a factor of two. We demonstrate that semi-analytical substructure modeling must include orbit integration in order to properly account for splashback haloes, which make up roughly half of the subhalo population. We show that the resolution limit of N-body simulations, rather than artificial disruption, is the primary cause of the radial bias in subhalo number density found in dark matter-only simulations. Hence, we conclude that the mass resolution remains the primary limitation of using such simulations to study subhaloes. Our model provides a fast, flexible, and accurate alternative to studying substructure statistics in the absence of both numerical resolution limits and artificial disruption.

show abstract

“…Mergers and mass accretion introduce non-thermal pressure support, which reduces the SZ signal detected. Thus, at fixed mass, the higher the mass accretion rate, the higher the non-thermal pressure fraction and the smaller the 𝑌 𝑆𝑍 value (Nelson et al 2014b;Green et al 2020). Thus, in general, we expect a negative correlation between the residual of the 𝑌 𝑆𝑍 − 𝑀 relation and the mass accretion rate.…”

Section: Reproducing the 𝑌 𝑆𝑍 − 𝑀 Relationmentioning

confidence: 90%

“…4, the relations of the respective cluster populations have slope and scatter within 1% of each other. For context, previous semi-analytic models which only take into account mass accretion but not baryonic physics and clumpy accretions like major mergers (Green et al 2020) reproduced 70% of the intrinsic scatter when compared to non-radiative simulations, and reproduced just 1/3 to 1/2 of the scatter when compared to full-physics simulations.…”

Section: Reproducing the 𝑌 𝑆𝑍 − 𝑀 Relationmentioning

confidence: 95%

“…However, the details of this scaling relation, such as its normalization and scatter, are sensitive to the mass accretion rate of the cluster because the turbulence introduced by structure formation processes adds additional pressure support (Krause et al 2011;Yu et al 2015;Green et al 2020). This causes a hydrostatic mass bias in measuring mass from SZ surveys (Planck Collaboration et al 2014;Nelson et al 2014a;Shi et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders

Rothschild,

Nagai,

Aung

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We develop a machine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. The algorithm uses a conditional variational autoencoder (CVAE) in the form of a convolutional neural network and is trained with SZ maps generated from the IllustrisTNG simulation. Our method can reproduce the details of the aspherical turbulent galaxy clusters with a resolution similar to hydrodynamic simulations while achieving the computational feasibility of semi-analytical maps, allowing us to generate over 10 5 mock SZ maps in 30 seconds on a laptop. We show that the individual images generated by the model are novel clusters (i.e. not found in the training set) and that the model accurately reproduces the effects of mass and mass accretion rate on the SZ images, such as scatter, asymmetry, and concentration, in addition to modeling merging sub-clusters. This work demonstrates the viability of machine-learning-based methods for producing the number of realistic, high-resolution maps of galaxy clusters necessary to achieve statistical constraints from future SZ surveys.

show abstract

Scatter in Sunyaev–Zel’dovich effect scaling relations explained by inter-cluster variance in mass accretion histories

Cited by 20 publications

References 160 publications

A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos

A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos

The tidal evolution of dark matter substructure – II. The impact of artificial disruption on subhalo mass functions and radial profiles

Emulating Sunyaev-Zeldovich Images of Galaxy Clusters using Auto-Encoders

Contact Info

Product

Resources

About