Related papers: A self-consistent data-driven model for determinin…
Radio campaigns using maser stellar beacons have provided crucial information to characterize Galactic stellar populations. Currently, the Bulge Asymmetries and Dynamical Evolution (BAaDE) project is surveying infrared (IR) color-selected…
New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is…
We calculate stellar masses for massive luminous galaxies at redshift 0.2-0.7 using the first two years of data from the Baryon Oscillation Spectroscopic Survey (BOSS). Stellar masses are obtained by fitting model spectral energy…
Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen…
We present the results of a novel application of Bayesian modelling techniques, which, although purely data driven, have a physically interpretable result, and will be useful as an efficient data mining tool. We base our studies on the…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves.…
The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2, MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper…
This project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and…
(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…
I briefly introduce a database of models that describe the evolution of star clusters in several broad-band photometric systems. Models are based on the latest Padova stellar evolutionary tracks - now including the alpha-enhanced case and…
Given their location on the Hertzsprung-Russell (HR) diagram, thoroughly characterized subgiant stars can place stringent constraints on a wide range of astrophysical problems. Accordingly, they are prime asteroseismic targets for the…
Stellar population synthesis (SPS) models are used to infer many galactic properties including star formation histories, metallicities, and stellar and dust masses. However, most SPS models neglect the effect of circumstellar dust shells…
We use empirical star formation histories (SFHs), measured from HST-based resolved star color-magnitude diagrams, as input into population synthesis codes to model the broadband spectral energy distributions (SEDs) of ~50 nearby dwarf…
We present SM-Net, a machine-learning model that learns a continuous spectral manifold from multiple high-resolution stellar libraries. SM-Net generates stellar spectra directly from the fundamental stellar parameters effective temperature…
We present MINESweeper, a tool to measure stellar parameters by jointly fitting observed spectra and broadband photometry to model isochrones and spectral libraries. This approach enables the measurement of spectrophotometric distances, in…
The efficiency of the transport of angular momentum and chemical elements inside intermediate-mass stars lacks proper calibration, thereby introducing uncertainties on a star's evolutionary pathway. Improvements require better estimation of…
We present a new approach to constrain galaxy physical parameters from the combined interpretation of stellar and nebular emission in wide ranges of observations. This approach relies on the Bayesian analysis of any type of galaxy spectral…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…
Context: Massive amounts of spectroscopic data obtained by stellar surveys are feeding an ongoing revolution in our knowledge of stellar and Galactic astrophysics. Analysing these data sets to extract the best possible astrophysical…