相关论文: AbacusHOD: A highly efficient extended multi-trace…
Accurate mock galaxy catalogues are crucial to validate analysis pipelines used to constrain dark energy models. We present a fast HOD-fitting method which we apply to the AbacusSummit simulations to create a set of mock catalogues for the…
Modern studies of galaxy formation rely heavily on numerical simulations, which in turn require tools to identify and track self-bound structures in stars and dark matter. In this paper, we present Bloodhound, a new halo tracking algorithm…
Analytical models of galaxy-halo connection such as the Halo Occupation Distribution (HOD) model have been widely used over the past decades as a means to intensively test perturbative models on quasi-linear scales. However, these models…
Tracking the formation and evolution of dark matter haloes is a critical aspect of any analysis of cosmological $N$-body simulations. In particular, the mass assembly of a halo and its progenitors, encapsulated in the form of its merger…
We develop a new Multi-Tracer Halo Occupation Distribution (\texttt{MTHOD}) framework for the galaxy distribution and apply it to the extended Baryon Oscillation Spectroscopic Survey (eBOSS) final data between $z=0.7-1.1$. We obtain a best…
We investigate theoretical systematics caused by the application of the halo occupation distribution (HOD) to the study of galaxy clustering at non-linear scales. To do this, we repeat recent cosmological analyses using extended HOD models…
This paper presents hep-aid, a modular Python library conceived for utilising, implementing, and developing parameter scan algorithms. Originally devised for sample-efficient, multi-objective active search approaches in computationally…
We describe a method for generating halo catalogues on the light cone using the \Abacus{AbacusSummit} suite of $N$-body simulations. The main application of these catalogues is the construction of realistic mock galaxy catalogues and weak…
We present diffHOD-IA, a fully differentiable implementation of a halo occupation distribution (HOD) model that incorporates galaxy intrinsic alignments (IA). Motivated by the diffHOD framework, we create a new implementation that extends…
Data from the ongoing \textit{Euclid} survey will map out billions of galaxies in the Universe, covering more than a third of the sky. This data will provide a wealth of information about the large-scale structure (LSS) of the Universe and…
We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies…
The Euclid mission, designed to map the geometry of the dark Universe, presents an unprecedented opportunity for advancing our understanding of the cosmos through its photometric galaxy cluster survey. This paper focuses on enhancing the…
A critical step to create realistic mock catalogs that support large-scale photometric and spectroscopic sky surveys is the production of cosmological simulations that accurately model the survey observables taking into account the…
We present a novel simulation-based hybrid emulator approach that maximally derives cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy clustering, with sufficient precision for DESI Year 1 (Y1) analysis.…
We present a proof-of-concept of a novel and fully Bayesian methodology designed to detect halos of different masses in cosmological observations subject to noise and systematic uncertainties. Our methodology combines the previously…
Building accurate and flexible galaxy-halo connection models is crucial in modeling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and…
The halo occupation distribution (HOD) approach has proven to be an effective method for modeling galaxy clustering and bias. In this approach, galaxies of a given type are probabilistically assigned to individual halos in N-body…
In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models.…
We present a toolkit, CosmoDS, designed to study cosmological models at the background level using dynamical system analysis within the Cobaya framework. Dynamical system analysis is a powerful mathematical approach for studying nonlinear…
This paper describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit…