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We have computed the evolving ultraviolet-millimeter spectral energy distribution (SED) produced by proto-galactic starbursts at high redshift, incorporating the chemical evolution of the interstellar medium in a consistent manner. Dust…
To exploit synergies between the Herschel Space Observatory and next generation radio facilities, we have extended the semi-empirical extragalactic radio continuum simulation of Wilman et al. (2008) to the mid- and far-infrared. Here we…
The spectral energy distribution of galaxies is a complex function of the star formation history and geometrical arrangement of stars and gas in galaxies. The computation of the radiative transfer of stellar radiation through the dust…
We aim to develop a model-driven deep learning approach to age determination, by training neural networks on stellar evolutionary grids. Contrary to the usual data-driven deep learning approach of using prior age estimates as training data,…
We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the…
Modelling carbon monoxide (CO) line radiation is computationally expensive for traditional numerical solvers, especially when applied to complex, three-dimensional stellar atmospheres. We present COEmuNet, a 3D convolutional neural network…
The spectral energy distributions (SEDs) of dust disks are widely used to infer dust properties (compositions and sizes) and disk structures (dust spatial distributions) which might be indicative of the presence or absence of planets or…
To improve the efficiency and sustainability of learning deep models, we propose CREST, the first scalable framework with rigorous theoretical guarantees to identify the most valuable examples for training non-convex models, particularly…
The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass…
Dust has long been identified as a barrier to measuring inherent galaxy properties. However, the link between dust and attenuation is not straightforward and depends on both the amount of dust and its distribution. Herschel imaging of…
With high-resolution infrared data becoming available that can probe the formation of high-mass stellar clusters for the first time, models that make testable predictions of these objects are necessary. We utilize a three-dimensional…
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as well as an application-specific augmentation accounting for…
We introduce the Dense Basis method for Spectral Energy Distribution (SED) fitting. It accurately recovers traditional SED parameters, including M$_*$, SFR and dust attenuation, and reveals previously inaccessible information about the…
Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of its high efficiency. Although various methods have been developed, template fitting is still adopted as one of the most…
To explore the connection between the global physical properties of galaxies and their far-infrared (FIR) spectral energy distributions (SEDs), we study the variation in the FIR SEDs of a set of hydrodynamically simulated galaxies that are…
We study the Spectral Energy Distributions, SEDs, (from FUV to MIR bands) of the first sizeable sample of 34 low-luminosity radio galaxies at high redshifts, selected in the COSMOS field. To model the SEDs we use two different…
We introduce ProSpect, a generative galaxy spectral energy distribution (SED) package that encapsulates the best practices for SED methodologies in a number of astrophysical domains. ProSpect comes with two popular families of stellar…
Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each…
Interpreting the spectral energy distributions (SEDs) of astrophysical objects with physically motivated models is computationally expensive. These models require solving coupled differential equations in high-dimensional parameter spaces,…
We investigate the pulsation-phase dependent properties of the circumstellar dust shell (CDS) of the OH/IR star OH104.9+2.4 based on radiative transfer modeling (RTM) using the code DUSTY. Our previous study concerning simultaneous modeling…