Related papers: The Parameter Space of Galaxy Formation
In this paper we define an observationally robust, multi-parameter space for the classification of nearby and distant galaxies. The parameters include luminosity, color, and the image-structure parameters: size, image concentration,…
Data analysis methods have always been of critical importance for quantitative sciences. In astronomy, the increasing scale of current and future surveys is driving a trend towards a separation of the processes of low-level data reduction…
Number counts of galaxies are re-analyzed using a semi-analytic model (SAM) of galaxy formation based on the hierarchical clustering scenario. We have determined the astrophysical parameters in the SAM that reproduce observations of nearby…
To facilitate the design and optimization of nanomaterials for a given application it is necessary to understand the relationship between structure and physical properties. For large nanomaterials, there is imprecise structural information…
We conduct a detailed comparison of broad-band spectral energy distributions of six z >= 5.5 galaxies against galaxies drawn from cosmological hydrodynamic simulations. We employ a new tool called SPOC, which constrains the physical…
The hot ($>10^6$ K) phase of the circumgalactic medium (CGM) contains a large fraction of baryons in galaxies. It also retains signatures of the processes that shaped the galaxies, such as feedback from active galactic nuclei (AGNs) and…
Using a semi-analytical approach we investigate the characteristics of predictions for the masses and metallicities of the baryonic matter in and around galaxies made by three galaxy formation models. These models represent three different…
We present global structural parameter measurements of 109,533 unique, H_F160W-selected objects from the CANDELS multi-cycle treasury program. Sersic model fits for these objects are produced with GALFIT in all available near-infrared…
With the launch and application of next-generation ground- and space-based telescopes, astronomy has entered the era of big data, necessitating more efficient and robust data analysis methods. Most traditional parameter estimation methods…
We present observations of 18 galaxies from the Galaxy And Mass Assembly (GAMA) survey made with the SPIRAL optical integral field unit (IFU) on the Anglo-Australian Telescope. The galaxies are selected to have a narrow range in stellar…
The present work is based on a parametric reconstruction of the deceleration parameter $q(z)$ in a model for the spatially flat FRW universe filled with dark energy and non-relativistic matter. In cosmology, the parametric reconstruction…
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between dark matter density fields…
Taking agent-based models (ABM) closer to the data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs combining supervised machine-learning and intelligent sampling to build a surrogate…
The formation of Hoag-type galaxies with central spheroidal galaxies and outer stellar rings has yet to be understood in astronomy. We consider that these unique objects were formed from the past interaction between elliptical galaxies and…
A model-independent or non-parametric approach for modeling a database has been widely used in cosmology. In these scenarios, the data has been used directly to reconstruct an underlying function. In this work, we introduce a novel…
Dynamical modelling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models.…
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)…
Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model. We use recent cosmological observations and Bayesian model averaging to derive tight limits on…
Methodologies for reducing the design-space dimensionality in shape optimization have been recently developed based on unsupervised machine learning methods. These methods provide reduced dimensionality representations of the design space,…
Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model…