arXiv subjects

David Huijser

Publications and source records attributed to David Huijser.

3 recordsLinked to original sources

arXiv2023

A Semi-Parametric Approach to Fitting Gas Pressure Profiles of Galaxy Clusters

This study focuses on modelling galaxy cluster gas profiles via a semi-parametric nodal approach. While traditional methods like the generalised Navarro-Frenk-White (gNFW) often encounter parameter degeneracy, our flexible node-based method precisely defines a cluster gas pressure profile. Using Planck space telescope data from the Coma region, our model, focused on the pressure-radius relationship, showcases enhanced flexibility over the gNFW. Bayesian analyses indicated an optimal five-node structure for the Coma cluster pressure profile.

astro-ph.CO↗

arXiv2017

Properties of the Affine Invariant Ensemble Sampler in high dimensions

We present theoretical and practical properties of the affine-invariant ensemble sampler Markov chain Monte Carlo method. In high dimensions the affine-invariant ensemble sampler shows unusual and undesirable properties. We demonstrate this with an $n$-dimensional correlated Gaussian toy problem with a known mean and covariance structure, and analyse the burn-in period. The burn-in period seems to be short, however upon closer inspection we discover the mean and the variance of the target distribution do not match the expected, known values. This problem becomes greater as $n$ increases. We therefore conclude that the affine-invariant ensemble sampler should be used with caution in high dimensional problems. We also present some theoretical results explaining this behaviour.

stat.CO↗

arXiv2015

Trans-Dimensional Bayesian Inference for Gravitational Lens Substructures

We introduce a Bayesian solution to the problem of inferring the density profile of strong gravitational lenses when the lens galaxy may contain multiple dark or faint substructures. The source and lens models are based on a superposition of an unknown number of non-negative basis functions (or "blobs") whose form was chosen with speed as a primary criterion. The prior distribution for the blobs' properties is specified hierarchically, so the mass function of substructures is a natural output of the method. We use reversible jump Markov Chain Monte Carlo (MCMC) within Diffusive Nested Sampling (DNS) to sample the posterior distribution and evaluate the marginal likelihood of the model, including the summation over the unknown number of blobs in the source and the lens. We demonstrate the method on two simulated data sets: one with a single substructure, and one with ten. We also apply the method to the g-band image of the "Cosmic Horseshoe" system, and find evidence for more than zero substructures. However, these have large spatial extent and probably only point to misspecifications in the model (such as the shape of the smooth lens component or the point spread function), which are difficult to guard against in full generality.

astro-ph.IM↗