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We present SPECULATOR - a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use principal component analysis to construct a…
We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these…
With the aim of building a data-set of spectral properties of well studied early-type galaxies showing emission lines, we present intermediate resolution spectra of 50 galaxies in the nearby Universe. The sample, which covers several of the…
Stellar population synthesis is a crucial methodology in astrophysics, enabling the interpretation of the integrated light of galaxies and stellar clusters. By combining empirical and/or theoretical libraries of the spectral energy…
The determination of stellar populations of galaxies are important for studying the formation and evolution of galaxies, because all galaxies contain many stars and they evolve with galaxies. Spectra data are usually used to determine the…
A new method, AMORE - based on a genetic algorithm optimizer, is presented for the automated study of colour-magnitude diagrams. The method combines several stellar population synthesis tools developed in the last decade by or in…
I describe very briefly the new libraries of empirical spectra of stars covering wide ranges of values of the atmospheric parameters Teff, log g, [Fe/H], as well as spectral type, that have become available in the recent past, among them…
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…
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not…
We constrain the stellar population properties of a sample of 52 massive galaxies, with stellar mass log Ms>10.5, over the redshift range 0.5<z<2 by use of observer-frame optical and near-infrared slitless spectra from HST's ACS and WFC3…
The estimated stellar masses of galaxies are widely used to characterize how the galaxy population evolves over cosmic time. If stellar masses can be estimated in a robust manner, free from any bias, global diagnostics such as the stellar…
Studies of resolved stellar populations in the Milky Way and nearby galaxies reveal an amazingly detailed and clear picture of galaxy evolution. Within the Local Group, the ability to probe the stellar populations of small and large…
Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy distribution (SED) fitting. However, this approach can be…
We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed…
Combining population synthesis models with simple Montecarlo simulations of stochastic effects in the number of stars occupying sparsely populated stellar evolutionary phases in the HRD, I show that the scatter observed in the photometric…
We introduce a new methodology for the direct extraction of galaxy physical parameters from multi-wavelength photometry and spectroscopy. We use semi-analytic models that describe galaxy evolution in the context of large scale cosmological…
We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and…
Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in recent…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
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,…