Related papers: Differentiable Stochastic Halo Occupation Distribu…
The halo occupation distribution (HOD) describes the bias between galaxies and dark matter by specifying (a) the probability P(N|M) that a halo of virial mass M contains N galaxies of a particular class and (b) the relative distributions of…
Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The…
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…
In this work, we develop a simulation-based model to predict the density split (DSS) and second-order shear and clustering statistics. A simulation-based model has the potential to model highly non-linear scales where current DSS models…
Adopting the framework of the Halo Occupation Distribution (HOD), we investigate the ability of galaxy clustering measurements to simultaneously constrain cosmological parameters and galaxy bias. Starting with a fiducial cosmological model…
Reinforcement Learning from Human Feedback (RLHF) is increasingly used to fine-tune diffusion models, but a key challenge arises from the mismatch between stochastic samplers used during training and deterministic samplers used during…
We investigate the viability of producing galaxy mock catalogues with COmoving Lagrangian Acceleration (COLA) simulations in Modified Gravity (MG) models employing the Halo Occupation Distribution (HOD) formalism. In this work, we focus on…
We use the TNG300 magneto-hydrodynamic simulation and mock catalogues built using subhalo abundance matching (SHAM) to study the origin of the redshift evolution of the halo occupation distribution (HOD). We analyse stellar-mass selected…
Modelling the distribution of neutral hydrogen (HI) in dark matter halos is important for studying galaxy evolution in the cosmological context. We use a novel approach to infer the HI-dark matter connection at the massive end ($m_{\rm HI}…
To improve the performance of full-shape analyses of large-scale structure, we consider using a halo occupation distribution (HOD)-informed prior for the effective field theory (EFT) nuisance parameters. We generate 320 000 mock galaxy…
We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a…
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…
Halo Occupation Distribution (HOD) models help us to connect observations and theory, by assigning galaxies to dark matter haloes. In this work we study one of the components of HOD models: the probability distribution function (PDF), which…
Accurate modeling of galaxy distributions is paramount for cosmological analysis using galaxy redshift surveys. However, this endeavor is often hindered by the computational complexity of resolving the dark matter halos that host these…
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating…
Clustering properties and peculiar velocities of halos in large-scale structure carry a wealth of cosmological information over a wide range of scales from linear to nonlinear scales. We use halo catalogs in a suite of high-resolution…
Effective out-of-distribution (OOD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that OOD detection methods are vulnerable to adversarial attacks,…
Neural networks extract features from data using stochastic gradient descent (SGD). In particular, higher-order input cumulants (HOCs) are crucial for their performance. However, extracting information from the $p$th cumulant of…
The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient…
Upcoming spectroscopic redshift surveys use emission line galaxies (ELGs) to trace the three-dimensional matter distributions with wider area coverage in the deeper Universe. Since the halos hosting ELGs are young and undergo infall towards…