相关论文: Exploring galactic properties with machine learnin…
We present the stellar mass - stellar metallicity relation for 3491 star-forming galaxies at $2 \lesssim z \lesssim 3$ using rest-frame far-ultraviolet (FUV) spectra from the Ly$\alpha$ Tomography IMACS Survey (LATIS). We fit stellar…
Our aim in this work is to answer, using simulated narrow-band photometry data, the following general question: What can we learn about galaxies from these new generation cosmological surveys? For instance, can we estimate stellar age and…
Galaxy physical properties-such as star formation rate (SFR), stellar mass, and gas-phase metallicity-are essential for population studies and evolutionary analyses. Deriving these quantities for billions of galaxies in modern imaging…
We have derived the uncertainties to be expected in the derivation of galaxy physical properties (star formation history, age, metallicity, reddening) when comparing broad-band photometry to the predictions of evolutionary synthesis models.…
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an…
We investigate the mass-metallicity relationship of star forming galaxies by analysing the absorption line spectra of $\sim$200,000 galaxies in the Sloan Digital Sky Survey. The galaxy spectra are stacked in bins of stellar mass and a…
Although starburst galaxies have relatively flat spectral energy distributions, their strong optical emission lines and near-UV continua make it very feasible to estimate their redshifts photometrically. In this work, I describe a…
To understand the formation and evolution of galaxies, it is important to have a full comprehension of the role played by the metallicity, star formation rate (SFR), morphology, and color. The interplay of these parameters at different…
We describe an equilibrium model that links the metallicity of low-redshift galaxies to stellar evolution models. It enables the testing of different stellar initial mass functions and metal yields against observed galaxy metallicities. We…
We present observational evidence for a stellar Fundamental Metallicity Relation (FMR), a smooth relation between stellar mass, star-formation rate (SFR) and the light-weighted stellar metallicity of galaxies (analogous to the…
A deep understanding of the Milky Way galaxy, its formation and evolution requires observations of huge numbers of stars. Stellar photometry, therefore, provides an economical method to obtain intrinsic stellar parameters. With the addition…
We study the metallicity properties of galaxies in the zCOSMOS sample between 0.2<z<0.8. At z<0.46, where Ha and [NII] are detected, we find the same dependence of metallicity on stellar mass and Star Formation Rate (SFR), the Fundamental…
(Abridged) We use 101 galaxies selected from the Nearby Field Galaxy Survey (NFGS) to investigate the effect of aperture size on the star formation rate, metallicity and reddening determinations for galaxies. We compare the star formation…
We present the first results of a project, LSD, aimed at obtaining spatially-resolved, near-infrared spectroscopy of a complete sample of Lyman-Break Galaxies at z~3. Deep observations with adaptive optics resulted in the detection of the…
Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional…
We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS…
We train a deep residual convolutional neural network (CNN) to predict the gas-phase metallicity ($Z$) of galaxies derived from spectroscopic information ($Z \equiv 12 + \log(\rm O/H)$) using only three-band $gri$ images from the Sloan…
Applying photometric catalogs to the study of the population of the Galaxy is obscured by the impossibility to map directly photometric colors into astrophysical parameters. Most of all-sky catalogs like ASCC or 2MASS are based upon…
This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy…
Based on the Sloan Digital Sky Survey Data Release 5 Galaxy Sample, we explore photometric morphology classification and redshift estimation of galaxies using photometric data and known spectroscopic redshifts. An unsupervised method,…