2011/05/31 by Joseph W. Richards, Ann B. Lee, Chad Schafer +2
Computer Science · Mathematics · Physics and Astronomy · #Advanced Vision and Imaging #Algorithm #Artificial intelligence #Astronomy and Astrophysical Research #Computer science #Estimation theory #Galaxies: Formation, Evolution, Phenomena #Grid #Hyperparameter optimization #Mathematics #Nonlinear system #Parameter space #Physics #Set (abstract data type) #Statistics #Support vector machine #astro-ph.IM #stat.AP
paper · pdf · doi:10.1214/11-aoas500
published as Annals of Applied Statistics 2012, Vol. 6, No. 1, 383-408 · Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2012/03/01 · arxiv created 2012/03/20 · arxiv updated 2012/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy’s stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters.