Related papers: The Parameter Space of Galaxy Formation
An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or…
We construct the Numerical Galaxy Catalog ($\nu$GC), based on a semi-analytic model of galaxy formation combined with high-resolution N-body simulations in a $\Lambda$-dominated flat cold dark matter ($\Lambda$CDM) cosmological model. The…
Computer simulation models are widely used to study complex physical systems. A related fundamental topic is the inverse problem, also called calibration, which aims at learning about the values of parameters in the model based on…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
Semi-analytic galaxy formation models are widely used to gain insight into the astrophysics of galaxy formation and in model testing, parameter space searching and mock catalogue building. In this work we present a new model for gas cooling…
We develop a novel statistical strong lensing approach to probe the cosmological parameters by exploiting multiple redshift image systems behind galaxies or galaxy clusters. The method relies on free-form mass inversion of strong lenses and…
We conduct Bayesian model inferences from the observed K-band luminosity function of galaxies in the local Universe, using the semi-analytic model (SAM) of galaxy formation introduced in Lu et al (2011). The prior distributions for the 14…
We apply a novel method with machine learning to calibrate sub-grid models within numerical simulation codes to achieve convergence with observations and between different codes. It utilizes active learning and neural density estimators.…
Semi-numerical models of reionization typically involve a large number of unknown parameters whose values are constrained by comparing with observations. Increasingly often, exploring this parameter space using semi-numerical simulations…
Using the N-body simulations of the AEMULUS Project, we construct an emulator for the non-linear clustering of galaxies in real and redshift space. We construct our model of galaxy bias using the halo occupation framework, accounting for…
We explore how the choice of galaxy formation model affects the predicted properties of high-redshift galaxies. Using the FLARES zoom resimulation strategy, we compare the EAGLE hydrodynamics model and the GALFORM, L-Galaxies, SC-SAM and…
Cosmological hydrodynamical simulations of galaxy evolution are increasingly able to produce realistic galaxies, but the largest hurdle remaining is in constructing subgrid models that accurately describe the behavior of stellar feedback.…
This paper proposes consistent estimators for transformation parameters in semiparametric models. The problem is to find the optimal transformation into the space of models with a predetermined regression structure like additive or…
With a statistical detection of the 21 cm signal fluctuations from the Epoch of Reionization (EoR) expected in the next few years, there is an interest in developing robust and precise techniques to constrain the underlying astrophysical…
We present a method to constrain galaxy parameters directly from three-dimensional data cubes. The algorithm compares directly the data with a parametric model mapped in $x,y,\lambda$ coordinates. It uses the spectral lines-spread function…
The parameter $f_{\textrm{NL}}$ measures the local non-Gaussianity in the primordial energy fluctuations of the Universe, with any deviation from $f_{\textrm{NL}}=0$ providing key constraints on inflationary models. Galaxy clustering is…
We develop a new formalism for modeling the formation and evolution of galaxies within a hierarchical universe. Similarly to standard semi-analytical models we trace galaxies inside dark-matter merger-trees. The formalism includes treatment…
The goal of this presentation is to build an efficient non-parametric Bayes classifier in the presence of large numbers of predictors. When analyzing such data, parametric models are often too inflexible while non-parametric procedures tend…
Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at…
We present a new model to compute the luminosity of emission lines in star forming galaxies and apply this in the semi-analytical galaxy formation code GALFORM. The model combines a pre-computed grid of HII region models with an empirical…