Structure finding in cosmological simulations: the state of affairs

By A. Knebe, Frazer R. Pearce, H. Lux, Y. Ascasibar, P. Behroozi, J. Casado, C. C. Moran, J. Diemand, Klaus Dolag, R. Domínguez-Tenreiro and 25 more. 2013

153 citations recorded by the source corpus.

The ever increasing size and complexity of data coming from simulations of cosmic structure formation demand equally sophisticated tools for their analysis. During the past decade, the art of object finding in these simulations has hence developed into an important discipline itself. A multitude of codes based upon a huge variety of methods and techniques have been spawned yet the question remained as to whether or not they will provide the same (physical) information about the structures of interest. Here we summarize and extent previous work of the 'halo finder comparison project': we investigate in detail the (possible) origin of any deviations across finders. To this extent, we decipher and discuss differences in halo-finding methods, clearly separating them from the disparity in definitions of halo properties. We observe that different codes not only find different numbers of objects leading to a scatter of up to 20 per cent in the halo mass and V-max function, but also that the particulars of those objects that are identified by all finders differ. The strength of the variation, however, depends on the property studied, e.g. the scatter in position, bulk velocity, mass and the peak value of the rotation curve is practically below a few per cent, whereas derived quantities such as spin and shape show larger deviations. Our study indicates that the prime contribution to differences in halo properties across codes stems from the distinct particle collection methods and - to a minor extent - the particular aspects of how the procedure for removing unbound particles is implemented. We close with a discussion of the relevance and implications of the scatter across different codes for other fields such as semi-analytical galaxy formation models, gravitational lensing and observables in general.

Subfields: Astronomy and Astrophysics, Instrumentation, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/c4e5c33984a2b508e9b8822ae0fd24c0dd01cbc2

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Citations in the corpus

Dark Matter Substructure in Numerical Simulations: A Tale of Discreteness Noise, Runaway Instabilities, and Artificial Disruption (2018, background): “In particular, a number of resolution and comparison studies (e.g., Springel et al. 2008; Onions et al. 2012; Knebe et al. 2013; van den Bosch & Jiang 2016; Griffen et al. 2016) have c© 2013 The Authors shown that subhalo mass functions are converged1 down to 50-100 particles per subhalo.”
Tracing the cosmic web (2017, methodology): “However, our comparison differs significantly from e.g. the seminal Santa Barbara comparison project (Frenk et al. 1999) or other tests of codes which purport to model the same physical process (e.g. Scannapieco et al. 2012; Knebe et al. 2013).”
Disruption of Dark Matter Substructure: Fact or Fiction? (2017, background): “This notion is based on the fact that simulations seem to yield consistent, converged results for the mass function of subhaloes above this mass resolution limit (e.g., Springel et al. 2008; Onions et al. 2012; Knebe et al. 2013; Cautun et al. 2014; van den Bosch & Jiang 2016; Griffen et al. 2016).”
SEMI-ANALYTIC GALAXY EVOLUTION (SAGE): MODEL CALIBRATION AND BASIC RESULTS (2016, result): “Knebe et al. (2011, 2013) assessed how and why the choice of (sub)halo finder can affect results, while complimentary studies investigated how the choice of merger tree code can change the derived structure formation histories and the consequences this has for semi-analytic models (Srisawat et al.…”
EMERGE – an empirical model for the formation of galaxies since z ∼ 10 (2017, background): “…only depends on gravity and the initial conditions have been measured very accurately (Planck Collaboration 2016), these simulations have converged and make accurate and definite predictions for the properties of dark matter haloes at all cosmic epochs (Frenk & White 2012; Knebe et al. 2013).”
UniverseMachine: The correlation between galaxy growth and dark matter halo assembly from z = 0−10 (2018, unknown): “Central halo finding is a more minor issue, especially when (as in this paper) vmax is used as the mass proxy (Knebe et al. 2011, 2013).”
The universality of the virial halo mass function and models for non-universality of other halo definitions (2015, result): “Since our virial overdensity at z=0 is closer to ∆ = 300 than ∆ = 200, this shows that different analyses converge provided one compares compatible halo identification schemes (Knebe et al. 2013).”