4,022 research outputs found
Prototype selection for parameter estimation in complex models
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.Comment: 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
Is \u27community\u27 important for Community Information Systems?
Community information systems have the power to transform communities. However, without fully understanding the pre-requisite factors affecting community information system viability, and the complex relationships between these factors, communities struggle to manage such projects in a way that leads to viable systems that deliver real benefits. This paper develops and presents a Model of Community Information System Viability Pre-requisite Factors, based on both existing literature and the study of three community information system projects. This Model represents the generic factors that inform viability (i.e. leadership, active membership, funding, awareness, and system design and functionality), and also considers the impact of community context. This study argues that the viability of a Community Information System cannot be considered in isolation. All factors are directly impacted by the value of the Community Information System to the community. Management can also heavily impact on the success of a Community Information System
Intrinsic Alignment in redMaPPer clusters -- II. Radial alignment of satellites toward cluster centers
We study the orientations of satellite galaxies in redMaPPer clusters
constructed from the Sloan Digital Sky Survey at to determine
whether there is any preferential tendency for satellites to point radially
toward cluster centers. We analyze the satellite alignment (SA) signal based on
three shape measurement methods (re-Gaussianization, de Vaucouleurs, and
isophotal shapes), which trace galaxy light profiles at different radii. The
measured SA signal depends on these shape measurement methods. We detect the
strongest SA signal in isophotal shapes, followed by de Vaucouleurs shapes.
While no net SA signal is detected using re-Gaussianization shapes across the
entire sample, the observed SA signal reaches a statistically significant level
when limiting to a subsample of higher luminosity satellites. We further
investigate the impact of noise, systematics, and real physical isophotal
twisting effects in the comparison between the SA signal detected via different
shape measurement methods. Unlike previous studies, which only consider the
dependence of SA on a few parameters, here we explore a total of 17 galaxy and
cluster properties, using a statistical model averaging technique to naturally
account for parameter correlations and identify significant SA predictors. We
find that the measured SA signal is strongest for satellites with the following
characteristics: higher luminosity, smaller distance to the cluster center,
rounder in shape, higher bulge fraction, and distributed preferentially along
the major axis directions of their centrals. Finally, we provide physical
explanations for the identified dependences, and discuss the connection to
theories of SA.Comment: 25 pages, 16 figures, 7 tables, accepted to MNRAS. Main statistical
analysis tool changed, with the results remain simila
Sherpa: a Mission-Independent Data Analysis Application
The ever-increasing quality and complexity of astronomical data underscores
the need for new and powerful data analysis applications. This need has led to
the development of Sherpa, a modeling and fitting program in the CIAO software
package that enables the analysis of multi-dimensional, multi-wavelength data.
In this paper, we present an overview of Sherpa's features, which include:
support for a wide variety of input and output data formats, including the new
Model Descriptor List (MDL) format; a model language which permits the
construction of arbitrarily complex model expressions, including ones
representing instrument characteristics; a wide variety of fit statistics and
methods of optimization, model comparison, and parameter estimation;
multi-dimensional visualization, provided by ChIPS; and new interactive
analysis capabilities provided by embedding the S-Lang interpreted scripting
language. We conclude by showing example Sherpa analysis sessions.Comment: To appear in Proc. SPIE Conf. 4477. 12 pages, 4 figure
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