| Presentations |
Alanna Connors
(with Aneta and Vinay)
08 Sep 2009 |
- Introduction to Astronomy for Statisticians
- [.pdf]
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- Movies:
- Full Sun rotating (Hinode/XRT) [.mov]
- flaring loops (Hinode/XRT) [.mov]
- transit of Mercury (Hinode/XRT) [.mov]
- Gamma-ray sky (Fermi) [.m4v]
- Black Hole at center of Milky Way (ESO/VLT) [.m4v]
- Discovery of Kuiper Belt object (APOD) [.gif]
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Brandon Kelly (CfA)
06 Oct 2009 |
- Hierarchical modeling of astronomical images and uncertainty in truncated data sets
- Abstract:
I will discuss two astronomical problems that I am working on, and
the statistical issues surrounding them. The first involves the
analysis of brightness images of astronomical objects with the goal
of recovering an 'image' of the physical properties of these objects.
Here, the primary goal is to infer from the brightness images how
the physical properties of the objects are correlated and spatially
distributed. The analysis is complicated by a two-level error
structure, having both additive and multiplicative errors, making
some of the model parameters nearly degenerate. The second problem
involves density estimation of a truncated data set, where the
truncation arises due to a data selection efficiency that varies
with an astronomical object's brightness (e.g., fainter things are
more difficult to detect). When the selection efficiency is known,
the analysis is straightfoward. However, when the selection efficiency
has some uncertainty in it, the likelihood function or posterior
distribution can be unstable. Currently, there does not appear to
be methods for accounting for uncertainty in the data selection
efficiency.
- Presentation [.pdf]
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Nathan Stein, Paul Baines
20 Oct 2009 |
- Markov Chain Monte Carlo Methods for Fitting Computer Models for Stellar Evolution (in three parts)
- Nathan Stein (Dept. of Statistics, Harvard U)
- Abstract:
Bayesian analysis of the evolution of star clusters presents
several computational challenges. Because physics-based models of
stellar evolution are implemented as computer models and are not
available in closed form, none of the conditional posterior
distributions are traditional named distributions. Moreover, the
posterior distributions of interest are high dimensional, strongly
correlated, and often multimodal. Markov chain Monte Carlo algorithms
can generate samples from these posterior distributions, but creating
reasonably efficient sampling algorithms requires advanced techniques.
- slides [.pdf]
- Paul Baines (Dept. of Statistics, Harvard U)
- Abstract:
The analysis of photometric data for stellar clusters provides an
example of both the statistical and computational challenges present
in many Astronomy applications. Typically, properties of stellar
clusters are estimated using Color-Magnitude Diagrams, whereby the
observed data are often simply compared to what one would expect under
a theoretical mapping from the model parameters to the observed data.
This mapping is determined by a set of isochrone tables, listing the
expected photometric measurements for a given set of input parameters.
To address many of the substantive questions in a coherent statistical
manner, we present a flexible hierarchical Bayesian model for the
analysis of stellar populations. The computation for the model is done
via Markov Chain Monte Carlo (MCMC), the standard tool of choice for
Bayesian computation. Both the complex dependence structure and the
peculiar nature of the isochrone mapping, however, present a
formidable challenge to standard statistical computation methods. In
the spirit of the Ancillary Sufficient Interweaving Scheme (ASIS) of
Yu & Meng (presented in a separate talk by Yu), we show how competing
parameterizations can be constructed and combined to help overcome the
weaknesses of individual schemes, and drastically improve the
efficiency and reliability of the computation.
- slides [.pdf]
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|
David Stenning / Jin Xu / Alex Blocker
3 Nov 2009 |
- David Stenning (UCI)
- Automatic Classification of Sunspot Groups Using SOHO/MDI Magnetogram and White-Light Images
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Abstract:
Sunspot groups are classified into four types: alpha, beta, beta-gamma,
and beta-gamma-detla. Currently, most sunspot group classification is
done manually by experts. This is a lengthy, labor-intensive, and somewhat
subjective process, necessitating the need for an automatic and accurate
procedure. We intend to use SOHO/MDI magnetogram and white-light images
to detect and classify sunspots into the appropriate group. The first
step in this process involves the extraction of white light data that
corresponds to the magnetogram images we have available. I will discuss
the progress I have made so far and address questions regarding the
automation of the extraction routine.
- presentation slides [.pdf]
-
- Jin Xu (UCI)
- Solar DEMs
- Abstract:
The wavelength distribution of light emitted from different regions of
the sun contains clues as to how the composition and temperature of the
sun varies across its surface. Decoding this information requires sophisticated
statistical techniques and detailed quantum physical calculations. Data
consists of images of the sun that record its intensity in each of a number
of wavelength bands. This "talk" will consist of a conversation about how
best to formulate the model to leverage both the data and quantum physics
to best understand composition and temperature images of the sun.
- presentation slides [.pdf]
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- Alex Blocker (HU)
- Event Detection in Time Series Databases with Robust Wavelet Model
- presentation slides [.pdf]
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Paul Baines, Yaming Yu
17 Nov 2009 |
- Markov Chain Monte Carlo Methods for Fitting Computer Models for Stellar Evolution (part two)
- Paul Baines (Dept. of Statistics, Harvard U)
- contd.
-
- Yaming Yu (Dept. of Statistics, UCI)
- Abstract:
The importance of a good parameterization for efficient MCMC
implementation has been repeatedly emphasized in the literature. For
a broad class of multi-level models, there exist two well-known
competing parameterizations: the centered parameterization and the non-
centered parameterization. We describe a surprisingly general and
powerful strategy for boosting MCMC efficiency by simply interweaving
---but not alternating---the two parameterizations. A Poisson time
series model for detecting changes in source intensity of photon
counts is used to illustrate the effectiveness of this strategy.
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Victoria Liublinska, Jin Xu
1 Dec 2009 |
- Solar and Stellar DEMs
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Nathan Stein, Jing Liu, Alanna Connors, David van Dyk
15 Dec 2009 |
- LIRA
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Fall/Winter 2004-2005
Siemiginowska, A. / Connors, A. / Kashyap, V. / Zezas, A. / Devor, J. / Drake, J. / Kolaczyk, E. / Izem, R. / Kang, H. / Yu, Y. / van Dyk, D. |
Fall/Winter 2005-2006
van Dyk, D. / Ratner, M. / Jin, J. / Park, T. / CCW / Zezas, A. / Hong, J. / Siemiginowska, A. & Kashyap, V. / Meng, X.-L. |
Fall/Winter 2006-2007
Lee, H. / Connors, A. / Protopapas, P. / McDowell, J., / Izem, R. / Blondin, S. / Lee, H. / Zezas, A., & Lee, H. / Liu, J.C. / van Dyk, D. / Rice, J.
|
Fall/Winter 2007-2008
Connors, A., & Protopapas, P. / Steiner, J. / Baines, P. / Zezas, A. / Aldcroft, T.
|
Fall/Winter 2008-2009
H. Lee /
A. Connors, B. Kelly, & P. Protopapas /
P. Baines /
A. Blocker /
J. Hong /
H. Chernoff /
Z. Li /
L. Zhu (Feb) /
A. Connors (Pt.1) /
A. Connors (Pt.2) /
L. Zhu (Mar) /
E. Kolaczyk /
V. Liublinska /
N. Stein
|
Fall/Winter 2009-2010
A.Connors /
B.Kelly /
N.Stein, P.Baines /
D.Stenning / J. Xu / A.Blocker /
P.Baines, Y.Yu /
V.Liublinska, J.Xu /
N.Stein, J.Liu, A.Connors, D.van Dyk /
|
