| Presentations |
Hyunsook Lee
9 Sep 2008
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- Computing the significance of non-nested models
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- slides [.pdf]
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Alanna Connors (Eureka Sci), Brandon Kelly (CfA), Pavlos Protopapas (CfA)
16 Sep 2008
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- "Some nice problems": Introducing High-Energy Astronomy and Astrophysics
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- Presentations:
- Alanna Connors [.pdf]
- Brandon Kelly [.pdf]
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group
23 Sep 2008 |
- proposals, programs, papers
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Paul Baines (HU)
30 Sep 2008 |
- Color-Magnitude Diagrams
- Abstract:
The properties of stars, and clusters of stars, have important implications
for understanding physical and stellar processes. We present a Hierarchical
Bayesian method for determining the mass, age and metallicity of stars from
photometric data. The method uses isochrone tables which map the 'expected'
data to the unknown parameters in a highly nonlinear manner. Our approach
allows for inference about individual star ages and masses, as well as
cluster-level properties. In this talk we will discuss one aspect of the
model that accounts for non-detections of stars. Some computational aspects
of the model will also be discussed.
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- Presentation [.pdf]
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Students
14 Oct 2008 |
- Stats grad students describe projects
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Alex Blocker (BU/HU)
21 Oct 2008 |
- Two Statistical Problems in X-ray Astronomy
- Abstract: I will discuss my work on two projects in x-ray astronomy: the
development of a hierarchical Bayesian replacement for "stacking" and
the analysis of events in x-ray light curves. For each problem, I will
outline the development of an improved model for the data and the
computational methods employed. I will also discuss the unique
challenges that each case has presented from a cultural perspective.
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- Presentation [.pdf]
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Jaesub Hong (CfA)
18 Nov 2008 |
- Peeking into the Early Universe with Coded-Aperture Imaging:
Energetic X-ray Imaging Survey Telescope (EXIST)
- Abstract:
A proposed Black Hole Finder probe, Energetic X-ray Imaging Survey
Telscope (EXIST) is re-designed to capture and identify high red-shift
Gamma-ray Burst (GRB) through X-ray imaging and onboard optical/IR
spectroscopy. EXIST will probe the early Universe using GRBs as cosmic
probe and survey black holes on all scales. I will review the current
mission concept for EXIST and its hard X-ray imaging technique,
coded-aperture imaging.
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- Presentation: [.pdf]; [.ppt]
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Herman Chernoff (HU)
02 Dec 2008 |
- Randomized Experiments and Hong's problem
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Li Zhan (HU)
16 Dec 2008 |
- A new tone of EM algorithm in the universe: MMT/Megacam Data
- Abstract:
In observing the objects in the space, there is a gap between
the ones can be observed through direct observation and the
one can be done through X-ray occultation. With the MMT/Megacam
survey data, we are trying to fill the gap, targeting at the objects
with diameter 200m-1km.
The MMT/Megacam traces the time evolution of the combined photon
number from both stars and the background. By employing the EM
algorithm, we aim at de-convoluting the effects of stars and finally
detecting the major changes in the flux of stars in the time
horizon. The change of flux of stars will give us invaluable
information on our targeted objects passing the star and thus
we can indirectly observe the targeted objects.
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- Presentation [.pdf]
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Li Zhu
03 Feb 2009 |
- RMFs
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Alanna Connors
17 Feb 2009 |
- Quantifying, Summarizing, and Representing 'Total' Uncertainties
in Image (and Spectral) 'Deconvolution'
- Abstract:
In 1998 Dixon et al. (1) used wavelets to demonsrtate a significant
mis-match between their all-sky gamma-ray data versus the best
physics-based models. They wrote: `The immediate question arises as
to the statistical significance of this feature. ... quantification of
object-wise significance (e.g., "this blob is significant at the
n\sigma level") are difficult.'
Ten years later, we have cracked the problem in general (2,3); but
many specific challenges remain.
We briefly describe the recent history. First, researchers tried using
flexible non-parametric models (NP; e.g. wavelets and the like; 4) to
represent an unknown 'true' sky image or spectrum. In both Bayesian
and frequentist methods, these are embedded in a likelihood framework
that includes the instrument 'smearing' ("forward-fitting"), with a
prior (or complexity penalty) acting as a regularizer. Second, rather
than using thresholding or an (EM) best-fit, Esch et al (2) pioneered
using MCMC to generate samples of the 'true' images. Third, we
included a physics-based model into the fit; with the flexible NP
component used only to capture any mis-match between one's best model
and the data. Fourth, extrapolating from recent classes of methods
that compare the distribution of NP model co-efficients with that
expected from noise, we came up with a generalized PPP method
(e.g. 5). Using simple low-dimensional summary statistics, we are able
to: 1/ test for significance and goodness of fit; 2/ set quantile
limits on the properties of any significant 'mis-match'; and 3/
translate and display the resulting (say) +/-5% credible regions back
to the image space for a different kind of object-wise significance,
and limits on shape --- all the while accounting for correlations
among the means of nearby bins or pixels. We do this all in the
low-count Poisson limit; but our methods are more generally
applicable.
Finally, in our Bayesian framework, we are able to incorporate
increasingly complex prior information in a hierarchical way. Thus,
we can also incorporate instrumental uncertainties, following the
approaches of Drake et al and Kashyap et al.
However many challenges remain. These include:
- Better summary statistics;
- Better, more robust and efficient ways to represent and incorporate
instrumental calibration uncertainties;
- Better representation of 'significance' than scatter plots or
histograms of 'null' vs 'interesting' results;
- More complex physics-models -- fitting at same time;
- Incorporating higher-level physics model-uncertainty;
- Keeping it a 'convex' (i.e. unimodal) problem when adding
different kinds of components;
- Higher dimensions (E and t as well as X and Y); and other coordinate
systems (Fermi's "Healpix", etc..)
- (1) Dixon, Hartman, Kolaczyk, et al, New Astronomy 3 (1998) 539.
- (2) Esch, D. N., Connors, A., Karovska, M., and van Dyk,
D. A. (2004). Ap.J. 610, 1213
- (3) Connors, A. and van Dyk, D. A. (2007). In SCMA IV (Editors:
E. Feigelson and G. Babu), vol. CS371, 101
- (4) Nowak, R. D. and Kolaczyk, E. D. (2000). IEEE Transactions on
Information Theory 46, 1811
- (5) Protassov, R., van Dyk, D. A., Connors, A., Kashyap, V., and
Siemiginowska, A. (2002). Ap.J. 571, 545.
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- Presentation slides [.pdf]
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Alanna Connors
03 Mar 2009 |
- Doubts and Challenges: The Untidiness of Real Examples
- Abstract:
We will again have the Geiger counter and radioactive source to use to
help define the problem, and to summarize the machinery we are
proposing to use as solutions (Bayes with physics-based plus
multi-resolution -- i.e. wavelet-like -- models, via MCMC and D.A.).
We will look at preliminary results from several kinds of Monte Carlo
tests, using our new methods. We will also introduce "skeptical
astronomers" with several kinds of doubts. As time permits, we will
also show several more examples of interesting data from X-ray and
Gamma-ray telescopes --- each with its own challenges.
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- Presentation slides [.pdf]
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Li Zhu
31 Mar 2009 |
- Wavelet analysis of RMFs
- Abstract:
In this presentation, I will talk about analyzing uncertainty of
redistribution matrix functions(RMFs) with wavelet decomposition. I used
wavelet (haar and db4) to do decomposition on both log(RMF) and the
difference between log(RMF) and log(default RMF). I will present the
characteristic of wavelet coefficients for both cases, especially the
similarity of wavelet coefficients between different true energies and
correlations between wavelet coefficients. After that I will suggest several
different Bayesian models which include base function and error term with
specific variance structure which I will use to do the further analysis.
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- Presentation:
- [.ppt]
- [.pdf]
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Erik Kolaczyk
07 Apr 2009 |
- Multiscale methods for Poisson count data: a review
- Abstract:
I will review a handful of methods designed for multiscale analysis of
Poisson count data, based on Haar wavelets, multiscale likelihood
factorizations, and piecewise polynomial bases on recursive partitions.
These methods were designed to translate the power of wavelet-based
methods in the standard Gaussian noise model to the context of count data.
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Victoria Liublinska
21 Apr 2009 |
- Differential Emission Measure analysis of high-resolution X-ray Spectra
- Abstract:
Access to substantial amount of data in the high-energy range gives us an
opportunity to extend our knowledge of stellar coronal composition and
temperature structure by analyzing the entire spectrum as a whole. Moreover,
data from detectors with high spectral resolution will provide additional
constraints on atomic data measurements being conducted in laboratories on
the ground. In particular, the best atomic emissivity databases created by
physicists still have missing, misplaced or poorly estimated lines and the
goal of our analysis is to provide ways of identifying lines that were
omitted and improve our estimates of stellar Differential Emission Measure
and plasma abundance by incorporating the information about them.
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- Presentation [.pdf]
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Nathan Stein
05 May 2009 |
- The White Dwarf Initial-Final Mass Relationship
- Abstract:
Stars lose mass during their evolution. A star's initial mass helps
determine both its rate of evolution and whether it becomes a white dwarf, a
black hole, or a neutron star. Since most stars end their lives as white
dwarfs, astronomers are eager to understand the relationship between white
dwarf masses and the initial masses of their progenitor stars, but large
theoretical and observational uncertainties remain. I will suggest a method
for obtaining inferences on the initial-final mass relationship by extending
statistical models for analyzing star cluster color-magnitude diagrams.
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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.
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Fall/Winter 2007-2008
Connors, A., & Protopapas, P. / Steiner, J. / Baines, P. / Zezas, A. / Aldcroft, T.
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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
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