The multidimensional dependence of halo bias in the eye of a machine: a tale of halo structure, assembly, and environment
Reads0
Chats0
TLDR
In this article, the bias is a multivariate function of halo properties that falls into three regimes: early-forming, low-mass and late-forming haloes, and the bias depends sensitively on the recent mass accretion history.Abstract:
We develop a novel approach in exploring the joint dependence of halo bias on multiple halo properties using Gaussian process regression. Using a $\Lambda$CDM $N$-body simulation, we carry out a comprehensive study of the joint bias dependence on halo structure, formation history and environment. We show that the bias is a multivariate function of halo properties that falls into three regimes. For massive haloes, halo mass explains the majority of bias variation. For early-forming haloes, bias depends sensitively on the recent mass accretion history. For low-mass and late-forming haloes, bias depends more on the structure of a halo such as its shape and spin. Our framework enables us to convincingly prove that $V_\mathrm{max}/V_\mathrm{vir}$ is a lossy proxy of formation time for bias modelling, whereas the mass, spin, shape and formation time variables are non-redundant with respect to each other. Combining mass and formation time largely accounts for the mass accretion history dependence of bias. Combining all the internal halo properties fully accounts for the density profile dependence inside haloes, and predicts the clustering variation of individual haloes to a $20\%$ level at $\sim 10\mathrm{Mpc}h^{-1}$. When an environmental density is measured outside $1\mathrm{Mpc}h^{-1}$ from the halo centre, it outperforms and largely accounts for the bias dependence on the internal halo structure, explaining the bias variation above a level of $30\%$.read more
Citations
More filters
Journal ArticleDOI
Cosmological reconstruction from galaxy light: neural network based light-matter connection
TL;DR: In this paper, the authors proposed a method to reconstruct the initial conditions of the universe using observed galaxy positions and luminosities under the assumption that the luminosity can be calibrated with weak lensing to give the mean halo mass.
Journal ArticleDOI
Cosmic web anisotropy is the primary indicator of halo assembly bias
TL;DR: In this paper, it was shown that the internal properties of dark matter haloes correlate with the large-scale halo clustering strength at fixed halo mass, and are also strongly affected by the local, non-linear cosmic web.
Journal ArticleDOI
The three causes of low-mass assembly bias
TL;DR: In this paper, the authors present a detailed analysis of the physical processes that cause halo assembly bias and show that splashback subhaloes are responsible for two thirds of the assembly bias signal, but do not account for the entire effect.
Journal ArticleDOI
Dissecting and modelling galaxy assembly bias
TL;DR: In this paper, a semi-analytic galaxy formation model was proposed to study the individual contributions of different secondary halo properties to the GAB signal, and the results showed that commonly used properties like the halo age or concentration amount to only 20-30% of the signal, while the smoothed matter density or the tidal anisotropy can explain the full level of GAB.
The Scale-Dependence of Halo Assembly Bias
TL;DR: In this article, the scale-dependent assembly bias of the two-point clustering of dark matter halos is studied and shown to be influenced by halo properties besides mass, a phenomenon referred to as halo assembly bias.
References
More filters
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Book
Gaussian Processes for Machine Learning
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Journal ArticleDOI
A Universal Density Profile from Hierarchical Clustering
TL;DR: In this article, the authors used high-resolution N-body simulations to study the equilibrium density profiles of dark matter halos in hierarchically clustering universes, and they found that all such profiles have the same shape, independent of the halo mass, the initial density fluctuation spectrum, and the values of the cosmological parameters.
Journal ArticleDOI
Simulations of the formation, evolution and clustering of galaxies and quasars
Volker Springel,Simon D. M. White,Adrian Jenkins,Carlos S. Frenk,Naoki Yoshida,Liang Gao,Julio F. Navarro,Robert J. Thacker,Darren J. Croton,John C. Helly,John A. Peacock,Shaun Cole,Peter A. Thomas,Hugh M. P. Couchman,August E. Evrard,Jörg M. Colberg,Frazers Pearce +16 more
TL;DR: It is shown that baryon-induced features in the initial conditions of the Universe are reflected in distorted form in the low-redshift galaxy distribution, an effect that can be used to constrain the nature of dark energy with future generations of observational surveys of galaxies.