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Many aspects of the evolution of stars, and in particular the evolution of binary stars, remain beyond our ability to model them in detail. Instead, we rely on observations to guide our often phenomenological models and pin down uncertain…

Solar and Stellar Astrophysics · Physics 2018-08-22 Robert G. Izzard , Ghina M. Halabi

We investigate the ability of spectroscopic techniques to yield realistic star formation histories (SFHs) for the bulges of spiral galaxies based on a comparison with their observed broadband colors. Full spectrum fitting to optical spectra…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-19 Lauren A. MacArthur , Michael McDonald , Stephane Courteau , J. Jesus Gonzalez

This paper addresses the inference of spatial dependence in the context of a recently proposed framework. More specifically, the paper focuses on the estimation of model parameters for a class of generalized Gibbs random fields, i.e.,…

Statistics Theory · Mathematics 2007-06-13 Samuel Elogne , Dionisis Hristopulos

In the era of vast spectroscopic surveys focusing on Galactic stellar populations, astronomers want to exploit the large quantity and good quality of data to derive their atmospheric parameters without losing precision from automatic…

Solar and Stellar Astrophysics · Physics 2017-12-13 M. Tsantaki , D. T. Andreasen , G. D. C. Teixeira , S. G. Sousa , N. C. Santos , E. Delgado-Mena , G. Bruzual

Sketching and stochastic gradient methods are arguably the most common techniques to derive efficient large scale learning algorithms. In this paper, we investigate their application in the context of nonparametric statistical learning.…

Machine Learning · Statistics 2019-01-25 Luigi Carratino , Alessandro Rudi , Lorenzo Rosasco

We introduce the Dense Basis method for Spectral Energy Distribution (SED) fitting. It accurately recovers traditional SED parameters, including M$_*$, SFR and dust attenuation, and reveals previously inaccessible information about the…

Astrophysics of Galaxies · Physics 2017-04-12 Kartheik G. Iyer , Eric Gawiser

We present a new set of stellar interior and synthesis models for predicting the integrated emission from stellar populations in star clusters and galaxies of arbitrary age and metallicity. This work differs from existing spectral synthesis…

Astrophysics · Physics 2009-11-10 Raul Jimenez , James MacDonald , James Dunlop , Paolo Padoan , John Peacock

Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact…

Instrumentation and Methods for Astrophysics · Physics 2015-06-05 Ross Fadely , David W. Hogg , Beth Willman

The star formation history (SFH) of galaxies allow us to investigate when galaxies formed their stars and assembled their mass. We can constrain the SFH with high level of precision from galaxies with resolved stellar populations, since we…

Astrophysics of Galaxies · Physics 2019-02-18 Maria Argudo-Fernández , Médéric Boquien , Shiyin Shen , Fangting Yuan , Jun Yin , Ruixiang Chang , Lei Hao

A flood of reliable seismic data will soon arrive. The migration to larger telescopes on the ground may free up 4-m class instruments for multi-site campaigns, and several forthcoming satellite missions promise to yield nearly uninterrupted…

Astrophysics · Physics 2007-05-23 Travis S. Metcalfe

Next generation photometric and spectroscopic surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting their potential requires a precise understanding of the…

We use the photometric information contained in individual pixels of 44,964 (0.019<z<0.125 and -23.5<M_r<-20.5) galaxies in the Fourth Data Release (DR4) of the Sloan Digital Sky Survey to investigate the effects of environment on galaxy…

Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem.…

Machine Learning · Computer Science 2018-08-22 Xingjian Shi , Dit-Yan Yeung

We propose a new method to infer the star formation histories of resolved stellar populations. With photometry one may plot observed stars on a colour-magnitude diagram (CMD) and then compare with synthetic CMDs representing different star…

Solar and Stellar Astrophysics · Physics 2015-06-16 J. J. Walmswell , J. J. Eldridge , B. J. Brewer , C. A. Tout

To understand the fundamental parameters of galaxy evolution, we investigated the minimum set of parameters that explain the observed galaxy spectra in the local Universe. We identified four latent variables that efficiently represent the…

Astrophysics of Galaxies · Physics 2023-11-30 Daiki Iwasaki , Suchetha Cooray , Tsutomu T. Takeuchi

High dimensional superposition models characterize observations using parameters which can be written as a sum of multiple component parameters, each with its own structure, e.g., sum of low rank and sparse matrices, sum of sparse and…

Machine Learning · Computer Science 2017-06-01 Qilong Gu , Arindam Banerjee

Stellar population studies provide unique clues to constrain galaxy formation models. So far, detailed studies based on absorption line strengths have mainly focused on the optical spectral range although many diagnostic features are…

Astrophysics of Galaxies · Physics 2022-05-27 Elham Eftekhari , Alexandre Vazdekis , Francesco La Barbera

The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium…

Solar and Stellar Astrophysics · Physics 2016-09-07 Kuldeep Verma , Shravan Hanasoge , Jishnu Bhattacharya , H M Antia , Ganapathy Krishnamurthi

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.…

Astrophysics · Physics 2009-10-22 A. C. Robin

We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems. The method is based on the invariances or stability of predicted results when known data is represented as…

Machine Learning · Statistics 2018-11-19 Patrick Chao , Tahereh Mazaheri , Bo Sun , Nicholas B. Weingartner , Zohar Nussinov