Related papers: Recovering Physical Parameters from Galaxy Spectra…
We adapt an existing data compression algorithm, MOPED, to the extraction of median-likelihood star formation (SF) histories from medium-resolution galaxy spectra. By focusing on the high-pass components of galaxy spectra, we minimize…
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…
We introduce VErsatile SPectral Analysis (VESPA): a new method which aims to recover robust star formation and metallicity histories from galactic spectra. VESPA uses the full spectral range to construct a galaxy history from synthetic…
I review the statistical techniques needed to extract information about physical parameters of galaxies from their observed spectra. This is important given the sheer size of the next generation of large galaxy redshift surveys. Going to…
We explore how observations relate to the physical properties of the emitting galaxies by post-processing a pair of merging $z\sim2$ galaxies from the cosmological, hydrodynamical simulation NewHorizon using LCARS (Light from Cloudy Added…
$ $Future surveys could obtain tighter constraints on the cosmological parameters with the galaxy power spectrum than with the Cosmic Microwave Background. However, the inclusion of multiple overlapping tracers, redshift bins, and more…
The spectral energy distribution (SED) of galaxies is essential for deriving fundamental properties like stellar mass and star formation history (SFH). However, conventional methods, including both parametric and non-parametric approaches,…
The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain…
The availability of high-quality spectra for a large number of galaxies in the SDSS survey allows for a more sophisticated extraction of information about their stellar populations than, e.g., the luminosity weighted age. Indeed,…
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…
Physical parameters of galaxies (as luminosity, stellar mass, age) are often derived by means of the model templates which best fit their spectro-photometric data. We have performed a quantitative test aimed at exploring the ability of this…
Spectral energy distribution (SED) models are widely used to infer the physical properties of galaxies from multi-wavelength photometry, but their accuracy is difficult to assess because the true properties of observed galaxies are…
MAx is a new tool to estimate parameters from stellar spectra. It is based on the maximum likelihood method, with the likelihood compressed in a way that the information stored in the spectral fluxes is conserved. The compressed data are…
We present PopSED, a framework for the population-level inference of galaxy properties from photometric data. Unlike the traditional approach of first analyzing individual galaxies and then combining the results to determine the physical…
Galaxy spectra are a rich source of kinematical information since the shapes of the absorption lines reflect the movement of stars along the line-of-sight. We present a technique to directly build a dynamical model for a galaxy by fitting…
In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to…
The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar…
Understanding how the physical properties of galaxies (e.g. their spectral type or age) evolve as a function of redshift relies on having an accurate representation of galaxy spectral energy distributions. While it has been known for some…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
The measurement of cosmological parameters is investigated in a representation of the least-action method that uses a redshift-space dataset to simultaneously constrain the real-space fields $\delta$,$\b v$. This method is robust in…