Presentations 

Hyunsook Lee 9 Sep 2008 
 Computing the significance of nonnested models

 slides [.pdf]


Alanna Connors (Eureka Sci), Brandon Kelly (CfA), Pavlos Protopapas (CfA) 16 Sep 2008 
 "Some nice problems": Introducing HighEnergy Astronomy and Astrophysics

 Presentations:
 Alanna Connors [.pdf]
 Brandon Kelly [.pdf]


group 23 Sep 2008 
 proposals, programs, papers


Paul Baines (HU) 30 Sep 2008 
 ColorMagnitude 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
clusterlevel properties. In this talk we will discuss one aspect of the
model that accounts for nondetections of stars. Some computational aspects
of the model will also be discussed.

 Presentation [.pdf]


Students 14 Oct 2008 
 Stats grad students describe projects


Alex Blocker (BU/HU) 21 Oct 2008 
 Two Statistical Problems in Xray Astronomy
 Abstract: I will discuss my work on two projects in xray astronomy: the
development of a hierarchical Bayesian replacement for "stacking" and
the analysis of events in xray 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.

 Presentation [.pdf]


Jaesub Hong (CfA) 18 Nov 2008 
 Peeking into the Early Universe with CodedAperture Imaging:
Energetic Xray Imaging Survey Telescope (EXIST)
 Abstract:
A proposed Black Hole Finder probe, Energetic Xray Imaging Survey
Telscope (EXIST) is redesigned to capture and identify high redshift
Gammaray Burst (GRB) through Xray 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 Xray imaging technique,
codedaperture imaging.

 Presentation: [.pdf]; [.ppt]


Herman Chernoff (HU) 02 Dec 2008 
 Randomized Experiments and Hong's problem


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 Xray occultation. With the MMT/Megacam
survey data, we are trying to fill the gap, targeting at the objects
with diameter 200m1km.
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 deconvoluting 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.

 Presentation [.pdf]


Li Zhu 03 Feb 2009 
 RMFs


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
mismatch between their allsky gammaray data versus the best
physicsbased models. They wrote: `The immediate question arises as
to the statistical significance of this feature. ... quantification of
objectwise 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 nonparametric 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' ("forwardfitting"), with a
prior (or complexity penalty) acting as a regularizer. Second, rather
than using thresholding or an (EM) bestfit, Esch et al (2) pioneered
using MCMC to generate samples of the 'true' images. Third, we
included a physicsbased model into the fit; with the flexible NP
component used only to capture any mismatch between one's best model
and the data. Fourth, extrapolating from recent classes of methods
that compare the distribution of NP model coefficients with that
expected from noise, we came up with a generalized PPP method
(e.g. 5). Using simple lowdimensional summary statistics, we are able
to: 1/ test for significance and goodness of fit; 2/ set quantile
limits on the properties of any significant 'mismatch'; and 3/
translate and display the resulting (say) +/5% credible regions back
to the image space for a different kind of objectwise 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
lowcount 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 physicsmodels  fitting at same time;
 Incorporating higherlevel physics modeluncertainty;
 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.

 Presentation slides [.pdf]


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 physicsbased plus
multiresolution  i.e. waveletlike  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 Xray and
Gammaray telescopes  each with its own challenges.

 Presentation slides [.pdf]


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.

 Presentation:
 [.ppt]
 [.pdf]


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 waveletbased
methods in the standard Gaussian noise model to the context of count data.


Victoria Liublinska 21 Apr 2009 
 Differential Emission Measure analysis of highresolution Xray Spectra
 Abstract:
Access to substantial amount of data in the highenergy 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.

 Presentation [.pdf]


Nathan Stein 05 May 2009 
 The White Dwarf InitialFinal 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 initialfinal mass relationship by extending
statistical models for analyzing star cluster colormagnitude diagrams.






Fall/Winter 20042005
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 20052006
van Dyk, D. / Ratner, M. / Jin, J. / Park, T. / CCW / Zezas, A. / Hong, J. / Siemiginowska, A. & Kashyap, V. / Meng, X.L. 
Fall/Winter 20062007
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 20072008
Connors, A., & Protopapas, P. / Steiner, J. / Baines, P. / Zezas, A. / Aldcroft, T.

Fall/Winter 20082009
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
