相关论文: Sensitivity analysis of a galaxy formation model
We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for $\sim 8$ million galaxies in the Hyper Suprime-Cam (HSC) Wide survey with $z \leq 0.75$ and $m \leq…
In this study, we aim to develop a new methodology to estimate the cross-correlation function and uncertainties and apply it to the analysis of magnification bias in galaxy surveys. We adopt a new methodological framework that uses a…
Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we…
We present a new approach to constrain galaxy physical parameters from the combined interpretation of stellar and nebular emission in wide ranges of observations. This approach relies on the Bayesian analysis of any type of galaxy spectral…
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…
The redshift-dependent relation between galaxy stellar mass and star formation rate (the Star-Forming Sequence, or SFS) is a key observational yardstick for galaxy assembly. We use the SAGAbg-A sample of background galaxies from the…
The consistency of the Starburst model for AGN is tested using the optical variability observed in large data bases of QSOs. Theoretical predictions for the variability--luminosity relationship and structure function are presented and…
Results from global sensitivity analysis (GSA) often guide the understanding of complicated input-output systems. Kernel-based GSA methods have recently been proposed for their capability of treating a broad scope of complex systems. In…
A major problem in extragalactic astronomy is the inability to distinguish in a robust, physical, and model independent way how galaxy populations are related to each other and to their formation histories. A similar, but distinct, and also…
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variability of a quantity of interest. One of the…
We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an…
We present a direct comparison between the observed star formation rate functions (SFRF) and the state-of-the-art predictions of semi-analytic models (SAM) of galaxy formation and evolution. We use the PACS Evolutionary Probe Survey (PEP)…
We present an accelerated calibration framework for semi-analytic galaxy formation models, demonstrated with Galacticus. Rather than fitting directly to properties such as the low-redshift stellar mass function (SMF) - which requires…
We study the impact of star formation and stellar feedback prescriptions on galaxy properties predicted by means of "stripped-down" versions of independently developed semi-analytic models (SAMs). These include cooling, star formation,…
Global sensitivity analysis aims at quantifying respective effects of input random variables (or combinations thereof) onto variance of a physical or mathematical model response. Among the abundant literature on sensitivity measures, Sobol'…
Reliability sensitivity analysis is concerned with measuring the influence of a system's uncertain input parameters on its probability of failure. Statistically dependent inputs present a challenge in both computing and interpreting these…
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…
Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the computational burden of processing high dimensional data. In this…
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…
Galaxy formation models, particularly semi-analytic models (SAMs), rely on differential equations with free parameters to describe the physical mechanisms governing galaxy formation and evolution. Traditionally, most SAMs calibrate these…