相关论文: Constraining Galaxy-Halo Connection Using Machine …
To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and halos accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the…
We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or are limited to…
The increase in the observed volume in cosmological surveys imposes various challenges on simulation preparations. Firstly, the volume of the simulations required increases proportionally to the observations. However, large-volume…
We apply machine learning, a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy-halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic…
We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
Context:Halo formation time, which quantifies the mass assembly history of dark-matter halos, directly impacts galaxy properties and evolution. Although not directly observable, it can be inferred through proxies like star formation history…
We develop a machine learning (ML) framework to populate large dark matter-only simulations with baryonic galaxies. Our ML framework takes input halo properties including halo mass, environment, spin, and recent growth history, and outputs…
We present a maximum-likelihood analysis of galaxy-galaxy lensing effects in galaxy clusters and in the field. The aim is to determine the accuracy and robustness of constraints that can be obtained on galaxy halo properties in both…
We place constraints on the average density (Omega_m) and clustering amplitude (sigma_8) of matter using a combination of two measurements from the Sloan Digital Sky Survey: the galaxy two-point correlation function, w_p, and the…
Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models…
It is common practice for methods that use galaxy clustering to constrain the galaxy-halo relationship, such as the halo occupation distribution (HOD) and/or conditional luminosity function (CLF), to assume that halo mass alone suffices to…
We present a new method that simultaneously solves for cosmology and galaxy bias on non-linear scales. The method uses the halo model to analytically describe the (non-linear) matter distribution, and the conditional luminosity function…
Machine learning (ML) techniques, in particular supervised regression algorithms, are a promising new way to use multiple observables to predict a cluster's mass or other key features. To investigate this approach we use the \textsc{MACSIS}…
Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very…
We make use of the IllustrisTNG cosmological, hydrodynamical simulations to test fundamental assumptions of the mass-based Halo Occupation Distribution (HOD) approach to modelling the galaxy-halo connection. By comparing the clustering of…
This research investigates how Machine Learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited…