Related papers: {\sc mirkwood:} Fast and Accurate SED Modeling Usi…
New far-infrared and submillimeter data are used to solidify and to extend to long wavelengths the empirical calibration of the infrared spectral energy distribution (SED) of normal star-forming galaxies. As was found by Dale et al. (2001),…
We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…
We present a proof-of-concept analysis of photometric redshifts with Bayesian priors on physical properties of galaxies. This concept is particularly suited for upcoming/on-going large imaging surveys, in which only several broad-band…
The objective of this paper is to understand the variance of the far-infrared (FIR) spectral energy distribution (SED) of the DustPedia galaxies, and its link with the stellar and dust properties. An interesting aspect of the dust emission…
We present a simple, largely empirical but physically motivated model, which is designed to interpret consistently multi-wavelength observations from large samples of galaxies in terms of physical parameters, such as star formation rate,…
Recent efforts on the modeling of the infrared spectral energy distributions (SEDs) of dwarf galaxies are summarised here. The characterisation of the dust properties in these low metallicity environments is just unfolding, as a result of…
As a logical next step in improving the radio-to-submm spectral index as a redshift indicator (Carilli & Yun 1999), we have investigated a technique of using the entire radio-to-FIR spectral energy distribution (SED) for deriving…
To understand and interpret the observed Spectral Energy Distributions (SEDs) of starbursts, theoretical or semi-empirical SED models are necessary. Yet, while they are well-founded in theory, independent verification and calibration of…
We derive physical parameters of galaxies from their observed spectrum, using MOPED, the optimized data compression algorithm of Heavens, Jimenez & Lahav 2000. Here we concentrate on parametrising galaxy properties, and apply the method to…
Interpreting observations of distant galaxies in terms of constraints on physical parameters - such as stellar mass, star-formation rate (SFR) and dust optical depth - requires spectral synthesis modelling. We analyse the reliability of…
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is…
Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come renders this task inhuman and Machine Learning (mainly Deep Learning) has been called to the…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
We construct an evolutionary spectral energy distribution (SED) model of a starburst region, from the ultraviolet to submillimetre wavelengths. This model allows us to derive the star formation rate, optical depth by dust and apparent…
Wide-field slitless spectroscopic galaxy surveys, such as the one performed by the upcoming Chinese Space Station Survey Telescope (CSST), are crucial for precision cosmology but present formidable data analysis challenges. Because spectra…
We apply the spectral energy distribution (SED) fitting code ProSpect to multiwavelength imaging for $\sim$7,000 galaxies from the GAMA survey at $z<0.06$, in order to extract their star formation histories. We combine a parametric…
In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we explore the use of a…
Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters…