Related papers: Predicting star formation properties of galaxies u…
Star Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFRs are usually estimated via spectroscopic observations requiring large amounts of telescope time. We explore an alternative approach based…
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…
All measurements of cosmic star formation must assume an initial distribution of stellar masses -- the stellar initial mass function -- in order to extrapolate from the star-formation rate measured for typically rare, massive stars (> 8…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
If we are to develop a comprehensive and predictive theory of galaxy formation and evolution, it is essential that we obtain an accurate assessment of how and when galaxies assemble their stellar populations, and how this assembly varies…
We apply Random Forest and XGBoost machine learning algorithms to determine which galaxy properties most effectively predict star formation and quenching in simulated galaxies. Using spatially-resolved data from approximately 63,000 annular…
I present a brief review of the stellar population properties of massive galaxies, focusing on early-type galaxies in particular, with emphasis on recent results from the ATLAS3D Survey. I discuss the occurrence of young stellar ages, cold…
The observable characteristics and subsequent evolution of young stellar populations is dominated by their massive stars. As our understanding of those massive stars and the factors affecting their evolution improves, so our interpretation…
In systems undergoing starbursts the evolution of the young stellar population is expected to drive changes in the emission line properties. This evolution is usually studied theoretically, with a combination of evolutionary synthesis…
If we are to develop a comprehensive and predictive theory of galaxy formation and evolution, it is essential that we obtain an accurate assessment of how and when galaxies assemble their stellar populations, and how this assembly varies…
The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This situation is indeed one that astronomers must contend with…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
I discuss recent theoretical work on the formation and evolution of galaxies paying particular attention to the ability of current models to make detailed comparisons with observations of the galaxy population both nearby and at high…
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…
Comparison with artificial galaxy models is essential for translating the incomplete and low signal-to-noise data we can obtain on astrophysical stellar populations to physical interpretations which describe their composition, physical…
Models of population synthesis for the Galaxy have been developed in order to understand galactic structure and evolution. They allow to test scenarii of evolution by comparisons between model predictions and observed distributions.…
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…
We have used the Spitzer Space Telescope to study the dust properties of a sample of star-forming dwarf galaxies. The differences in the mid-infrared spectral energy distributions for these galaxies which, in general, are low metallicity…